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"title": "Detection of presynaptic terminals on dendritic spines in double labeling confocal images",
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"abstract": "For the analysis of learning processes and the underlying changes of the shape of excitatory synapses (spines), 3-D volume samples of selected dendritic segments are scanned by a confocal laser scanning microscope. The images are unsharp because of the (direction dependent) resolution limit. A deconvolution on image data is not sufficient for the resolution needed. Therefore a parametric model is used to reconstruct the dendrite and the spines. The parameter estimation of model is done in a two step approach. First, rough center axes of dendrite and spines are found by a growing model which can be adjusted interactively. In a second step the model parameters are optimized during an iterative process. To estimate the deviation between the microscope image and the model, the model is sampled with the same resolution as the microscope image and convolved by the microscope point spread function (psf). The result is a accurate model of dendrite and spines. The model fitting process is comparable with a deconvolution but with a limited number of model parameters and stable results without strong distortions by PSF. The associated presynaptic terminal can be detected in a second image channel inside a region of interest (roi) on spine position. Morphological features of spines from geometrical model and from second channel roi are combined for statistical analysis.",
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"content": "For the analysis of learning processes and the underlying changes of the shape of excitatory synapses (spines), 3-D volume samples of selected dendritic segments are scanned by a confocal laser scanning microscope. The images are unsharp because of the (direction dependent) resolution limit. A deconvolution on image data is not sufficient for the resolution needed. Therefore a parametric model is used to reconstruct the dendrite and the spines. The parameter estimation of model is done in a two step approach. First, rough center axes of dendrite and spines are found by a growing model which can be adjusted interactively. In a second step the model parameters are optimized during an iterative process. To estimate the deviation between the microscope image and the model, the model is sampled with the same resolution as the microscope image and convolved by the microscope point spread function (psf). The result is a accurate model of dendrite and spines. The model fitting process is comparable with a deconvolution but with a limited number of model parameters and stable results without strong distortions by PSF. The associated presynaptic terminal can be detected in a second image channel inside a region of interest (roi) on spine position. Morphological features of spines from geometrical model and from second channel roi are combined for statistical analysis.",
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"fullName": "Gerald Krell",
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{
"affiliation": "Otto-von-Guericke-University Magdeburg",
"fullName": "Wladimir Ovtscharoff",
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"affiliation": "Otto-von-Guericke-University Magdeburg",
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"proceeding": {
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"title": "Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000",
"acronym": "cbms",
"groupId": "1000153",
"volume": "0",
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"year": "2000",
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"article": {
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"doi": "10.1109/CBMS.2000.856908",
"title": "Use of Shape Models to Search Digitized Spine X-rays",
"normalizedTitle": "Use of Shape Models to Search Digitized Spine X-rays",
"abstract": "We are building a biomedical information resource consisting of digitized x-ray images and associated textual data from national health surveys. This resource, the Web-based Medical Information Retrieval System, or WebMIRS, is currently in beta test. In a future WebMIRS system, we plan to have not only text and raw image data, but quantitative anatomical feature information derived from the images and capability to retrieve images based on image characteristics, either alone or in conjunction with text descriptions associated with the images. Our archive consists of data collected in the second and third National Health and Nutrition Examination Surveys (NHANES), conducted by the National Center for Health Statistics. For the NHANES II survey, the records contain information for approximately 20,000 participants. Each record contains about two thousand data points, including demographic information, answers to health questionnaires, anthropometric information, and the results of a physician's examination. In addition, approximately 10,000 cervical spines and 7,000 lumbar spine x-rays were collected. WebMIRS makes the text and images retrievable. Only raw images are returned; no quantitative or descriptive information about the images is stored in the database. We are conducting research into the problem of automatically or semi-automatically segmenting spine vertebrae in these images and determining vertebral boundaries with enough accuracy to be useful in classifying the vertebrae into categories of interest to researchers in osteoarthritis.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We are building a biomedical information resource consisting of digitized x-ray images and associated textual data from national health surveys. This resource, the Web-based Medical Information Retrieval System, or WebMIRS, is currently in beta test. In a future WebMIRS system, we plan to have not only text and raw image data, but quantitative anatomical feature information derived from the images and capability to retrieve images based on image characteristics, either alone or in conjunction with text descriptions associated with the images. Our archive consists of data collected in the second and third National Health and Nutrition Examination Surveys (NHANES), conducted by the National Center for Health Statistics. For the NHANES II survey, the records contain information for approximately 20,000 participants. Each record contains about two thousand data points, including demographic information, answers to health questionnaires, anthropometric information, and the results of a physician's examination. In addition, approximately 10,000 cervical spines and 7,000 lumbar spine x-rays were collected. WebMIRS makes the text and images retrievable. Only raw images are returned; no quantitative or descriptive information about the images is stored in the database. We are conducting research into the problem of automatically or semi-automatically segmenting spine vertebrae in these images and determining vertebral boundaries with enough accuracy to be useful in classifying the vertebrae into categories of interest to researchers in osteoarthritis.",
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"normalizedAbstract": "We are building a biomedical information resource consisting of digitized x-ray images and associated textual data from national health surveys. This resource, the Web-based Medical Information Retrieval System, or WebMIRS, is currently in beta test. In a future WebMIRS system, we plan to have not only text and raw image data, but quantitative anatomical feature information derived from the images and capability to retrieve images based on image characteristics, either alone or in conjunction with text descriptions associated with the images. Our archive consists of data collected in the second and third National Health and Nutrition Examination Surveys (NHANES), conducted by the National Center for Health Statistics. For the NHANES II survey, the records contain information for approximately 20,000 participants. Each record contains about two thousand data points, including demographic information, answers to health questionnaires, anthropometric information, and the results of a physician's examination. In addition, approximately 10,000 cervical spines and 7,000 lumbar spine x-rays were collected. WebMIRS makes the text and images retrievable. Only raw images are returned; no quantitative or descriptive information about the images is stored in the database. We are conducting research into the problem of automatically or semi-automatically segmenting spine vertebrae in these images and determining vertebral boundaries with enough accuracy to be useful in classifying the vertebrae into categories of interest to researchers in osteoarthritis.",
"fno": "04840255",
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{
"affiliation": "National Library of Medicine",
"fullName": "L. Rodney Long",
"givenName": "L. Rodney",
"surname": "Long",
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{
"affiliation": "National Library of Medicine",
"fullName": "George R. Thoma",
"givenName": "George R.",
"surname": "Thoma",
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{
"proceeding": {
"id": "12OmNwErpHy",
"title": "Proceedings Computer Animation 2001. Fourteenth Conference on Computer Animation",
"acronym": "ca",
"groupId": "1000121",
"volume": "0",
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"article": {
"id": "12OmNzmtWw6",
"doi": "10.1109/CA.2001.982391",
"title": "Human spine posture estimation from video images based on connected vertebra spheres model",
"normalizedTitle": "Human spine posture estimation from video images based on connected vertebra spheres model",
"abstract": "This paper reports a method for estimating human spine posture using the front and side views of a human body taken by a video camera. We present here a new 3D model to estimate the spine posture using connected vertebra spheres. In this model, each vertebra forming the spine is approximated as a sphere. The spine is approximated as a series of spheres connecting each other. Each sphere has the control points to represent the outer shape of the body. The spine posture is estimated by moving the sphere and the control points. The estimation is performed by calculating the matching ratio between a projected image of the model and an input image. X-ray CT slices are used for the construction of the model. We applied the proposed method to real human images. The experimental results show that our 3D model worked reasonably well for the estimation of human spine posture based on real human images.",
"abstracts": [
{
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"content": "This paper reports a method for estimating human spine posture using the front and side views of a human body taken by a video camera. We present here a new 3D model to estimate the spine posture using connected vertebra spheres. In this model, each vertebra forming the spine is approximated as a sphere. The spine is approximated as a series of spheres connecting each other. Each sphere has the control points to represent the outer shape of the body. The spine posture is estimated by moving the sphere and the control points. The estimation is performed by calculating the matching ratio between a projected image of the model and an input image. X-ray CT slices are used for the construction of the model. We applied the proposed method to real human images. The experimental results show that our 3D model worked reasonably well for the estimation of human spine posture based on real human images.",
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"normalizedAbstract": "This paper reports a method for estimating human spine posture using the front and side views of a human body taken by a video camera. We present here a new 3D model to estimate the spine posture using connected vertebra spheres. In this model, each vertebra forming the spine is approximated as a sphere. The spine is approximated as a series of spheres connecting each other. Each sphere has the control points to represent the outer shape of the body. The spine posture is estimated by moving the sphere and the control points. The estimation is performed by calculating the matching ratio between a projected image of the model and an input image. X-ray CT slices are used for the construction of the model. We applied the proposed method to real human images. The experimental results show that our 3D model worked reasonably well for the estimation of human spine posture based on real human images.",
"fno": "00982391",
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"affiliation": "Dept. of Comput. Sci. & Eng., Nagoya Univ., Japan",
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"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
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"title": "Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment",
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"abstract": "Heatmap regression has been used for landmark localization for quite a while now. Most of the methods use a very deep stack of bottleneck modules for heatmap classification stage, followed by heatmap regression to extract the keypoints. In this paper, we present a single dendritic CNN, termed as Pose Conditioned Dendritic Convolution Neural Network (PCD-CNN), where a classification network is followed by a second and modular classification network, trained in an end to end fashion to obtain accurate landmark points. Following a Bayesian formulation, we disentangle the 3D pose of a face image explicitly by conditioning the landmark estimation on pose, making it different from multi-tasking approaches. Extensive experimentation shows that conditioning on pose reduces the localization error by making it agnostic to face pose. The proposed model can be extended to yield variable number of landmark points and hence broadening its applicability to other datasets. Instead of increasing depth or width of the network, we train the CNN efficiently with Mask-Softmax Loss and hard sample mining to achieve upto 15% reduction in error compared to state-of-the-art methods for extreme and medium pose face images from challenging datasets including AFLW, AFW, COFW and IBUG.",
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"content": "Heatmap regression has been used for landmark localization for quite a while now. Most of the methods use a very deep stack of bottleneck modules for heatmap classification stage, followed by heatmap regression to extract the keypoints. In this paper, we present a single dendritic CNN, termed as Pose Conditioned Dendritic Convolution Neural Network (PCD-CNN), where a classification network is followed by a second and modular classification network, trained in an end to end fashion to obtain accurate landmark points. Following a Bayesian formulation, we disentangle the 3D pose of a face image explicitly by conditioning the landmark estimation on pose, making it different from multi-tasking approaches. Extensive experimentation shows that conditioning on pose reduces the localization error by making it agnostic to face pose. The proposed model can be extended to yield variable number of landmark points and hence broadening its applicability to other datasets. Instead of increasing depth or width of the network, we train the CNN efficiently with Mask-Softmax Loss and hard sample mining to achieve upto 15% reduction in error compared to state-of-the-art methods for extreme and medium pose face images from challenging datasets including AFLW, AFW, COFW and IBUG.",
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"normalizedAbstract": "Heatmap regression has been used for landmark localization for quite a while now. Most of the methods use a very deep stack of bottleneck modules for heatmap classification stage, followed by heatmap regression to extract the keypoints. In this paper, we present a single dendritic CNN, termed as Pose Conditioned Dendritic Convolution Neural Network (PCD-CNN), where a classification network is followed by a second and modular classification network, trained in an end to end fashion to obtain accurate landmark points. Following a Bayesian formulation, we disentangle the 3D pose of a face image explicitly by conditioning the landmark estimation on pose, making it different from multi-tasking approaches. Extensive experimentation shows that conditioning on pose reduces the localization error by making it agnostic to face pose. The proposed model can be extended to yield variable number of landmark points and hence broadening its applicability to other datasets. Instead of increasing depth or width of the network, we train the CNN efficiently with Mask-Softmax Loss and hard sample mining to achieve upto 15% reduction in error compared to state-of-the-art methods for extreme and medium pose face images from challenging datasets including AFLW, AFW, COFW and IBUG.",
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"Learning Artificial Intelligence",
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"3 D Pose",
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"Landmark Estimation",
"Localization Error",
"Bayesian Formulation",
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"affiliation": null,
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"title": "2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)",
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"abstract": "Vertebrae segmentation is a key component of quantitative spine image analysis, which can directly detect and evaluate vertebrae fractures, and indirectly support modeling and monitoring of spinal aging process. There are two main segmentation methods in the past: one is to use 2D CNN for segmentation, and then stack along a certain axis; the other is to preform 3D CNN segmentation directly. However, the former does not utilize the correlation information between the upper and lower slices lead to low segmentation accuracy, the latter has significant shortcomings such as mass of parameters, dependent data and slow segmentation speed. In this paper, a 3D segmentation algorithm using multi-perspectives 2D spine images is proposed. The proposed algorithm does not require a large amount of data, which can segment the spine boundary more accurately. Specifically, the 2D slices from three perspectives are segmented respectively, and then the three segmentation results are fused. It is different from previous algorithms that ignore the information from different perspectives. In this paper, we adopt three perspectives for mutual supervision learning to obtain the 3D information of the spine. In particular, we add the shape flow information in order to obtain more accurate boundary segmentation in the network. The model obtains 83.4% PA on the standard spine data. Similarly, the proposed algorithm still has obvious advantages in BraTS 2018 brain tumor segmentation task, and the proposed algorithm still achieved 80.0% PA in the case of a small amount of data.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Vertebrae segmentation is a key component of quantitative spine image analysis, which can directly detect and evaluate vertebrae fractures, and indirectly support modeling and monitoring of spinal aging process. There are two main segmentation methods in the past: one is to use 2D CNN for segmentation, and then stack along a certain axis; the other is to preform 3D CNN segmentation directly. However, the former does not utilize the correlation information between the upper and lower slices lead to low segmentation accuracy, the latter has significant shortcomings such as mass of parameters, dependent data and slow segmentation speed. In this paper, a 3D segmentation algorithm using multi-perspectives 2D spine images is proposed. The proposed algorithm does not require a large amount of data, which can segment the spine boundary more accurately. Specifically, the 2D slices from three perspectives are segmented respectively, and then the three segmentation results are fused. It is different from previous algorithms that ignore the information from different perspectives. In this paper, we adopt three perspectives for mutual supervision learning to obtain the 3D information of the spine. In particular, we add the shape flow information in order to obtain more accurate boundary segmentation in the network. The model obtains 83.4% PA on the standard spine data. Similarly, the proposed algorithm still has obvious advantages in BraTS 2018 brain tumor segmentation task, and the proposed algorithm still achieved 80.0% PA in the case of a small amount of data.",
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"normalizedAbstract": "Vertebrae segmentation is a key component of quantitative spine image analysis, which can directly detect and evaluate vertebrae fractures, and indirectly support modeling and monitoring of spinal aging process. There are two main segmentation methods in the past: one is to use 2D CNN for segmentation, and then stack along a certain axis; the other is to preform 3D CNN segmentation directly. However, the former does not utilize the correlation information between the upper and lower slices lead to low segmentation accuracy, the latter has significant shortcomings such as mass of parameters, dependent data and slow segmentation speed. In this paper, a 3D segmentation algorithm using multi-perspectives 2D spine images is proposed. The proposed algorithm does not require a large amount of data, which can segment the spine boundary more accurately. Specifically, the 2D slices from three perspectives are segmented respectively, and then the three segmentation results are fused. It is different from previous algorithms that ignore the information from different perspectives. In this paper, we adopt three perspectives for mutual supervision learning to obtain the 3D information of the spine. In particular, we add the shape flow information in order to obtain more accurate boundary segmentation in the network. The model obtains 83.4% PA on the standard spine data. Similarly, the proposed algorithm still has obvious advantages in BraTS 2018 brain tumor segmentation task, and the proposed algorithm still achieved 80.0% PA in the case of a small amount of data.",
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"authors": [
{
"affiliation": "Qinghai University,Department of Computer Technology and Application,Xining,China,810016",
"fullName": "Jiali Cui",
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{
"affiliation": "Qinghai University,School of Mechanical Engineering,Xining,China,810016",
"fullName": "Dedong Gao",
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{
"affiliation": "Qinghai University,School of Mechanical Engineering,Xining,China,810016",
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"affiliation": "Qinghai University,School of Mechanical Engineering,Xining,China,810016",
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"affiliation": "Qinghai University,School of Mechanical Engineering,Xining,China,810016",
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{
"proceeding": {
"id": "1fTgF9x78sw",
"title": "2019 IEEE Visualization Conference (VIS)",
"acronym": "vis",
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"year": "2019",
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"article": {
"id": "1fTgFVHfgPe",
"doi": "10.1109/VISUAL.2019.8933795",
"title": "Interactive Dendritic Spine Analysis Based on 3D Morphological Features",
"normalizedTitle": "Interactive Dendritic Spine Analysis Based on 3D Morphological Features",
"abstract": "Dendritic spines are submicron scale protrusions on neuronal dendrites that form the postsynaptic sites of excitatory neuronal inputs. The morphological changes of dendritic spines reflect alterations in physiological conditions and are further indicators of various neuropsychiatric conditions. However, due to the highly dynamic and heterogeneous nature of spines, accurate measurement and object analysis of spine morphology is a major challenge in neuroscience research. Here, we propose an interactive 3D dendritic spine analysis system that displays 3D rendering of spines and plots the high-dimensional features extracted from the 3D mesh of spines in three graph types (parallel coordinate plot, radar plot, and 2D scatter plot with t-Distributed Stochastic Neighbor Embedding). With this system, analysts can effectively explore and analyze the dendritic spine in a 3D manner with high-dimensional features. For the system, we have constructed a set of morphological high-dimensional features from the 3D mesh of dendritic spines.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Dendritic spines are submicron scale protrusions on neuronal dendrites that form the postsynaptic sites of excitatory neuronal inputs. The morphological changes of dendritic spines reflect alterations in physiological conditions and are further indicators of various neuropsychiatric conditions. However, due to the highly dynamic and heterogeneous nature of spines, accurate measurement and object analysis of spine morphology is a major challenge in neuroscience research. Here, we propose an interactive 3D dendritic spine analysis system that displays 3D rendering of spines and plots the high-dimensional features extracted from the 3D mesh of spines in three graph types (parallel coordinate plot, radar plot, and 2D scatter plot with t-Distributed Stochastic Neighbor Embedding). With this system, analysts can effectively explore and analyze the dendritic spine in a 3D manner with high-dimensional features. For the system, we have constructed a set of morphological high-dimensional features from the 3D mesh of dendritic spines.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "Dendritic spines are submicron scale protrusions on neuronal dendrites that form the postsynaptic sites of excitatory neuronal inputs. The morphological changes of dendritic spines reflect alterations in physiological conditions and are further indicators of various neuropsychiatric conditions. However, due to the highly dynamic and heterogeneous nature of spines, accurate measurement and object analysis of spine morphology is a major challenge in neuroscience research. Here, we propose an interactive 3D dendritic spine analysis system that displays 3D rendering of spines and plots the high-dimensional features extracted from the 3D mesh of spines in three graph types (parallel coordinate plot, radar plot, and 2D scatter plot with t-Distributed Stochastic Neighbor Embedding). With this system, analysts can effectively explore and analyze the dendritic spine in a 3D manner with high-dimensional features. For the system, we have constructed a set of morphological high-dimensional features from the 3D mesh of dendritic spines.",
"fno": "08933795",
"keywords": [
"Biomedical Optical Imaging",
"Data Visualisation",
"Dendrites",
"Feature Extraction",
"Medical Image Processing",
"Neurophysiology",
"Rendering Computer Graphics",
"Stochastic Processes",
"Interactive Dendritic Spine Analysis",
"3 D Morphological Features",
"Neuronal Dendrites",
"Spine Morphology",
"Spine Analysis System",
"High Dimensional Features",
"Postsynaptic Sites",
"Excitatory Neuronal Inputs",
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"Neuropsychiatric Conditions",
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"T Distributed Stochastic Neighbor Embedding",
"Morphological High Dimensional Features",
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"Neck",
"Head",
"Image Color Analysis",
"Two Dimensional Displays",
"Rendering Computer Graphics",
"Shape",
"Biomedical And Medical Visualization",
"Coordinated And Multiple Views",
"User Interfaces"
],
"authors": [
{
"affiliation": "Ulsan National Institute of Science and Technology",
"fullName": "JunYoung Choi",
"givenName": "JunYoung",
"surname": "Choi",
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{
"affiliation": "Seoul National University College of Medicine",
"fullName": "Sang-Eun Lee",
"givenName": "Sang-Eun",
"surname": "Lee",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Seoul National University College of Medicine",
"fullName": "Eunji Cho",
"givenName": "Eunji",
"surname": "Cho",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Tokyo",
"fullName": "Yutaro Kashiwagi",
"givenName": "Yutaro",
"surname": "Kashiwagi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Tokyo",
"fullName": "Shigeo Okabe",
"givenName": "Shigeo",
"surname": "Okabe",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Seoul National University College of Medicine",
"fullName": "Sunghoe Chang",
"givenName": "Sunghoe",
"surname": "Chang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Ulsan National Institute of Science and Technology",
"fullName": "Won-Ki Jeong",
"givenName": "Won-Ki",
"surname": "Jeong",
"__typename": "ArticleAuthorType"
}
],
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"year": "2019",
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"abstract": "Distributed geological prospecting projection management system based Web service has been designed to speed up the digitization development of Gold Geological Branch of Chinese Armed Police Force (GGBCAPF). In this paper, the development of distributed application system has been briefed and the overall design and key technology research of geological prospecting projection management system have been introduced briefly. It is proved that the system which has accelerated the informatization, digitization and routinization of geological prospecting, has changed the way of geological data gather, storage, process and has improved the effectiveness of geological prospecting work played a significant role in geological prospecting of CCGCAPF.",
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"abstract": "Integrating different remote sensing data to obtain the geological information as much as possible for petroleum geological exploration is the main object of this research project. Radar images have very different characteristics from visible/infrared satellite remote sensing images in both physical properties and image mechanization. Data fusion technology is used to merge Landsat TM data and Radarsat SAR data, and several methods of image fusion and information extraction are tried to compare their results. The result images show that the different detail can be highlighted by different approach, and the method of image multiplication followed by PCA transform obtains good effects by analyzing carefully the each character if these two images. The integrated images give some new understand for the geological structure of the area and reveal some buried structures and faults through comprehensive geological interpretation.",
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"content": "Integrating different remote sensing data to obtain the geological information as much as possible for petroleum geological exploration is the main object of this research project. Radar images have very different characteristics from visible/infrared satellite remote sensing images in both physical properties and image mechanization. Data fusion technology is used to merge Landsat TM data and Radarsat SAR data, and several methods of image fusion and information extraction are tried to compare their results. The result images show that the different detail can be highlighted by different approach, and the method of image multiplication followed by PCA transform obtains good effects by analyzing carefully the each character if these two images. The integrated images give some new understand for the geological structure of the area and reveal some buried structures and faults through comprehensive geological interpretation.",
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"title": "A Methodology for Automatically 3D Geological Modeling Based on Geophysical Data Grids",
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"abstract": "Using 3D visualization models to exhibit geological structure has become a trend in geological studies. Compared to 2D geological mapping, 3D geological modeling is dependent on more geological sampling information. In many cases, however, the geological sampling information is difficult to acquire by drilling (especially for deep subsurface information). Geophysical methods (e.g., Gravity, seismic, and electric) have become the major tools in geological modeling. Because the geophysical data are recorded in a data grid, people must extract the geological information from various data grids acquired through different geophysical methods and subsequently integrate the information to manually construct a 3D geological model. This approach usually causes inconvenience and inefficiencies in practice. Therefore, we propose a methodology of automatically 3D geological modeling based on geophysical data grids. The method first constructs visualization models from different geophysical data grids and subsequently integrates these models for interpretation using mapping rules learned from physical properties of rock samples measured in a laboratory and finally converts the interpreted visualization model to a 3D geological model. With the application in the practical work, the result demonstrates that the methodology can effectively solve problems of 3D geological modeling in the case of enriched geophysical data lacking sufficient geological sampling information.",
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"content": "Using 3D visualization models to exhibit geological structure has become a trend in geological studies. Compared to 2D geological mapping, 3D geological modeling is dependent on more geological sampling information. In many cases, however, the geological sampling information is difficult to acquire by drilling (especially for deep subsurface information). Geophysical methods (e.g., Gravity, seismic, and electric) have become the major tools in geological modeling. Because the geophysical data are recorded in a data grid, people must extract the geological information from various data grids acquired through different geophysical methods and subsequently integrate the information to manually construct a 3D geological model. This approach usually causes inconvenience and inefficiencies in practice. Therefore, we propose a methodology of automatically 3D geological modeling based on geophysical data grids. The method first constructs visualization models from different geophysical data grids and subsequently integrates these models for interpretation using mapping rules learned from physical properties of rock samples measured in a laboratory and finally converts the interpreted visualization model to a 3D geological model. With the application in the practical work, the result demonstrates that the methodology can effectively solve problems of 3D geological modeling in the case of enriched geophysical data lacking sufficient geological sampling information.",
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"abstract": "Geological Map Database Management System has been developed based on component GIS in this paper. It integrated the DEM and the National basic geological map. It can realize real-time transformation of two-dimensional plane and three- dimensional display on the geological map, extraction data by custom area, and geological analysis of slope, aspect, section lines. It can improve the map’s information, administrative levels and accuracy, and also widely used in geological prospecting, water, earthquake, engineering geology, etc. Therefore, the developed system will provide a scientific basis in the respect of optimizing geological environment, the early warning of geological disasters zoning, utilization of underground space and planning for urban construction and development.",
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"content": "Geological Map Database Management System has been developed based on component GIS in this paper. It integrated the DEM and the National basic geological map. It can realize real-time transformation of two-dimensional plane and three- dimensional display on the geological map, extraction data by custom area, and geological analysis of slope, aspect, section lines. It can improve the map’s information, administrative levels and accuracy, and also widely used in geological prospecting, water, earthquake, engineering geology, etc. Therefore, the developed system will provide a scientific basis in the respect of optimizing geological environment, the early warning of geological disasters zoning, utilization of underground space and planning for urban construction and development.",
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"affiliation": "China University of Geosciences,School of Earth Sciences and Resources,Beijing,China,100083",
"fullName": "Lele Han",
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"surname": "Han",
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"affiliation": "Chinese Academy of Geological Sciences,SinoProbe Center,Beijing,China,100037",
"fullName": "Xuan-Hua Chen",
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"affiliation": "Chinese Academy of Geological Sciences,SinoProbe Center,Beijing,China,100037",
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"affiliation": "Shandong Zhengyuan Digital City Construction Co., Lid,Yantai,China,264000",
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"abstract": "Geological disaster monitoring can provide useful information for natural disaster early warning. In this study, the existing geological disaster monitoring sensor technology, monitoring technology is summarized. The theory, function and characteristics of the existing geological disaster monitoring sensors are introduced in detail. Several frequently used geological disaster monitoring technologies are described, and their advantages and disadvantages are compared and analyzed. Finally, the development trend of geological disaster monitoring technology is predicted.",
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"fullName": "Han Zhang",
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"title": "Construction of Early Warning Model of Geological Disasters Based on Data Mining",
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"abstract": "In view of the problem that traditional geological disaster monitoring methods cannot adapt to the development of big data, an early warning model of geological disasters based on data mining was proposed. Firstly, the function of the early warning system was determined, and the system included login, data mining and cleaning, early warning and other functions. Secondly, the early warning evaluation system of geological disasters was constructed from the three aspects of susceptibility, hazard and risk, and then the risk was early warned and evaluated by analytic hierarchy process and fuzzy evaluation method. The experimental results showed that in the actual prediction, the early warning results obtained by the early warning model of geological disasters based on data mining are consistent with the actual situation. Moreover, compared with the historical data, the prediction accuracy of the medium above disaster data in the early warning model reaches 87.433%. Therefore, the early warning model of geological disasters based on data mining can accurately predict geological disasters.",
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{
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"content": "In view of the problem that traditional geological disaster monitoring methods cannot adapt to the development of big data, an early warning model of geological disasters based on data mining was proposed. Firstly, the function of the early warning system was determined, and the system included login, data mining and cleaning, early warning and other functions. Secondly, the early warning evaluation system of geological disasters was constructed from the three aspects of susceptibility, hazard and risk, and then the risk was early warned and evaluated by analytic hierarchy process and fuzzy evaluation method. The experimental results showed that in the actual prediction, the early warning results obtained by the early warning model of geological disasters based on data mining are consistent with the actual situation. Moreover, compared with the historical data, the prediction accuracy of the medium above disaster data in the early warning model reaches 87.433%. Therefore, the early warning model of geological disasters based on data mining can accurately predict geological disasters.",
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"keywords": [
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"normalizedAbstract": "In this paper, a novel approach, namely kernel projection vector space (KPVS), is proposed for radar target recognition using high-resolution range profile (HRRP). First, the HRRP samples are mapped into a high-dimensional feature space using nonlinear mapping. Second, the kernel projection vectors, are obtained by kernel discriminant analysis. Then, for each class, the kernel projection vector space is formed using all the training kernel projection vectors of class. Finally, the minimum hyper plane distance classifier (MHDC) is used for classification. The aim of KPVS method is to represent the feature area of target using kernel projection vector space, and effectively measure the distance between the test HRRP and feature area via minimum hyper plane distance (MHD) metric. The experimental results of measured data show that the proposed method has better performance of recognition than KPCA and KFDA.",
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"title": "Three-dimensional reconstruction algorithm based on inverse perspective transformation",
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"abstract": "In order to solve the problems of difficulty and low accuracy in three-dimensional reconstruction based on 2D images, this paper proposes a three-dimensional reconstruction method based on inverse perspective transformation for 2D images. This method through the detection and segmentation in the images of the water wall tube welding area, a two-dimensional image by inverse perspective transformation welding area of the reduction in the three-dimensional world, is obtained by cubic spline interpolation on flow surface area boundary point, using three-dimensional kriging interpolation method involves cloud model, the point cloud model for grid operation, reconstruction of three-dimensional welding zone model. The experimental results show that this method is simple and efficient, and can effectively carry out three-dimensional reconstruction based on 2d images, which provides a new way of thinking for three-dimensional reconstruction of 2D images.",
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"Inverse Perspective Transformation",
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"affiliation": "Harbin Engineering University,Harbin,China",
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"abstract": "Currently those algorithms to mine association rules only pay attention to one aspect of efficiency or accuracy or correlativity respectively, even they ignore mining principal factors among all the correlativity. Thus, there seems a paradox among efficiency, accuracy and correlativity. In order to resolve to this conflict, a novel algorithm based on Probability estimate and principal component analysis is proposed to mine the association rules from database with the high correlativity and the high confidence. Probability estimate reduce the times of database scanning so as to increase efficiency and accuracy, and principal component analysis helps us to know which factors have most influence to event rate so as to distinguish correlativity. Experimental results have demonstrated that our algorithms are efficient accurate and correlativity.",
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"content": "Currently those algorithms to mine association rules only pay attention to one aspect of efficiency or accuracy or correlativity respectively, even they ignore mining principal factors among all the correlativity. Thus, there seems a paradox among efficiency, accuracy and correlativity. In order to resolve to this conflict, a novel algorithm based on Probability estimate and principal component analysis is proposed to mine the association rules from database with the high correlativity and the high confidence. Probability estimate reduce the times of database scanning so as to increase efficiency and accuracy, and principal component analysis helps us to know which factors have most influence to event rate so as to distinguish correlativity. Experimental results have demonstrated that our algorithms are efficient accurate and correlativity.",
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"abstract": "This paper describes the simultaneous spectrophotometric determination of copper, zinc, nickel and cobalt in water with 5-Br-PADAP. The absorbance spectra obtained were processed with wavelet transform (WT) for removal of noise from the spectra. In order to eliminate spectral interferences, calculation of the metal concentrations in mixtures was performed using principal component regression (PCR). The results showed that the combination of MT and PCR allowed more accurate and precious spectrophotometric determination of these ions than traditional PCR approach.",
"abstracts": [
{
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"content": "This paper describes the simultaneous spectrophotometric determination of copper, zinc, nickel and cobalt in water with 5-Br-PADAP. The absorbance spectra obtained were processed with wavelet transform (WT) for removal of noise from the spectra. In order to eliminate spectral interferences, calculation of the metal concentrations in mixtures was performed using principal component regression (PCR). The results showed that the combination of MT and PCR allowed more accurate and precious spectrophotometric determination of these ions than traditional PCR approach.",
"__typename": "ArticleAbstractType"
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],
"normalizedAbstract": "This paper describes the simultaneous spectrophotometric determination of copper, zinc, nickel and cobalt in water with 5-Br-PADAP. The absorbance spectra obtained were processed with wavelet transform (WT) for removal of noise from the spectra. In order to eliminate spectral interferences, calculation of the metal concentrations in mixtures was performed using principal component regression (PCR). The results showed that the combination of MT and PCR allowed more accurate and precious spectrophotometric determination of these ions than traditional PCR approach.",
"fno": "3819b745",
"keywords": [
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"Zinc",
"Nickel",
"Cobalt",
"Principal Component Regression",
"Wavelet Transform",
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"fullName": "Xiaoguo Ma",
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"doi": "10.1109/ICDMW51313.2020.00023",
"title": "Synthetic Data by Principal Component Analysis",
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"abstract": "In statistical disclosure control, releasing synthetic data implies difficulty in identifying individual records, since the value of synthetic data is different from original data. We propose two methods of generating synthetic data using principal component analysis: orthogonal transformation (linear method) and sandglass-type neural networks (nonlinear method). While the typical generation method of synthetic data by multiple imputation requires existence of common variables between population and survey data, our proposed method can generate synthetic data without common variables. Additionally, the linear method can explicitly evaluate information loss as the ratio of discarded eigenvalues. We generate synthetic data by the proposed method for decathlon data and evaluate four information loss measures: our proposed information loss measure, mean absolute error for each record, mean absolute error of mean of each variable, and mean absolute error of covariance between variables. We find that information loss in the linear method is less than that in the nonlinear method.",
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"content": "In statistical disclosure control, releasing synthetic data implies difficulty in identifying individual records, since the value of synthetic data is different from original data. We propose two methods of generating synthetic data using principal component analysis: orthogonal transformation (linear method) and sandglass-type neural networks (nonlinear method). While the typical generation method of synthetic data by multiple imputation requires existence of common variables between population and survey data, our proposed method can generate synthetic data without common variables. Additionally, the linear method can explicitly evaluate information loss as the ratio of discarded eigenvalues. We generate synthetic data by the proposed method for decathlon data and evaluate four information loss measures: our proposed information loss measure, mean absolute error for each record, mean absolute error of mean of each variable, and mean absolute error of covariance between variables. We find that information loss in the linear method is less than that in the nonlinear method.",
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"fno": "901200a101",
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"Data Privacy",
"Eigenvalues And Eigenfunctions",
"Neural Nets",
"Principal Component Analysis",
"Synthetic Data",
"Principal Component Analysis",
"Linear Method",
"Sensitivity Analysis",
"Neural Networks",
"Sociology",
"Measurement Uncertainty",
"Loss Measurement",
"Eigenvalues And Eigenfunctions",
"Principal Component Analysis",
"Statistical Disclosure Control",
"Synthetic Data",
"Principal Component Analysis",
"Sandglass Type Neural Networks"
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"authors": [
{
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"abstract": "Industrial Control Systems (ICS) are increasingly facing the threat of False Data Injection (FDI) attacks. As an emerging intrusion detection scheme for ICS, process-based Intrusion Detection Systems (IDS) can effectively detect the anomalies caused by FDI attacks. Specifically, such IDS establishes anomaly detection model which can describe the normal pattern of industrial processes, then perform real-time anomaly detection on industrial process data. However, this method suffers low detection accuracy due to the complexity and instability of industrial processes. That is, the process data inherently contains sophisticated nonlinear spatial-temporal correlations which are hard to be explicitly described by anomaly detection model. In addition, the noise and disturbance in process data prevent the IDS from distinguishing the real anomaly events. In this paper, we propose an Anomaly Detection approach based on Robust Spatial-temporal Modeling (AD-RoSM). Concretely, to explicitly describe the spatial-temporal correlations within the process data, a neural based state estimation model is proposed by utilizing 1D CNN for temporal modeling and multi-head self attention mechanism for spatial modeling. To perform robust anomaly detection in the presence of noise and disturbance, a composite anomaly discrimination model is designed so that the outputs of the state estimation model can be analyzed with a combination of threshold strategy and entropy-based strategy. We conducted extensive experiments on two benchmark ICS security datasets to demonstrate the effectiveness of our approach.",
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"content": "Industrial Control Systems (ICS) are increasingly facing the threat of False Data Injection (FDI) attacks. As an emerging intrusion detection scheme for ICS, process-based Intrusion Detection Systems (IDS) can effectively detect the anomalies caused by FDI attacks. Specifically, such IDS establishes anomaly detection model which can describe the normal pattern of industrial processes, then perform real-time anomaly detection on industrial process data. However, this method suffers low detection accuracy due to the complexity and instability of industrial processes. That is, the process data inherently contains sophisticated nonlinear spatial-temporal correlations which are hard to be explicitly described by anomaly detection model. In addition, the noise and disturbance in process data prevent the IDS from distinguishing the real anomaly events. In this paper, we propose an Anomaly Detection approach based on Robust Spatial-temporal Modeling (AD-RoSM). Concretely, to explicitly describe the spatial-temporal correlations within the process data, a neural based state estimation model is proposed by utilizing 1D CNN for temporal modeling and multi-head self attention mechanism for spatial modeling. To perform robust anomaly detection in the presence of noise and disturbance, a composite anomaly discrimination model is designed so that the outputs of the state estimation model can be analyzed with a combination of threshold strategy and entropy-based strategy. We conducted extensive experiments on two benchmark ICS security datasets to demonstrate the effectiveness of our approach.",
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"abstract": "As closed Industrial Control Systems (ICS) gradually evolve toward networking, ICS data and operational processes can be easily tampered with by attackers, causing industrial control equipment to fail or become damaged. Depending on the characteristics of ICS business logic stability, this paper proposes a novel two-level anomaly detection framework to ensure that system data and business logic are safe and reliable. Specifically, basic information is obtained from network traffic. In our framework, the first-level detection uses convolutional neural network (CNN) to feature extraction and anomaly identification. In the second-level detection, we propose a process state transfer algorithm. The feature extracted by the CNN model is invoked as the input of the algorithm to construct the normal state process transfer model of ICS. The model detects whether the current data meets the normal state transition process of the system, and may find unknown attacks or 0-day attacks. Finally, through laboratory gas pipeline network system verification, we found that the anomaly detection framework combined with the two methods has more outstanding performance than several current latest technologies.",
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"abstract": "In the past years, industrial networks have become increasingly interconnected and opened to private or public networks. This leads to an increase in efficiency and manageability, but also increases the attack surface. Industrial networks often consist of legacy systems that have not been designed with security in mind. In the last decade, an increase in attacks on cyber-physical systems was observed, with drastic consequences on the physical work. In this work, attack vectors on industrial networks are categorised. A real-world process is simulated, attacks are then introduced. Finally, two machine learning-based methods for time series anomaly detection are employed to detect the attacks. Matrix Profiles are employed more successfully than a predictor Long Short-Term Memory network, a class of neural networks.",
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"content": "In the past years, industrial networks have become increasingly interconnected and opened to private or public networks. This leads to an increase in efficiency and manageability, but also increases the attack surface. Industrial networks often consist of legacy systems that have not been designed with security in mind. In the last decade, an increase in attacks on cyber-physical systems was observed, with drastic consequences on the physical work. In this work, attack vectors on industrial networks are categorised. A real-world process is simulated, attacks are then introduced. Finally, two machine learning-based methods for time series anomaly detection are employed to detect the attacks. Matrix Profiles are employed more successfully than a predictor Long Short-Term Memory network, a class of neural networks.",
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"abstract": "Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. However, analysis of multiple security data could provide more comprehensive and system-wide anomaly detection in industrial networks. In this paper, we present an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWaT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors. However, multi-source analysis is more robust because it can detect anomalies from multiple sources simultaneously, while achieving comparable precision for each of the sources.",
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"content": "Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. However, analysis of multiple security data could provide more comprehensive and system-wide anomaly detection in industrial networks. In this paper, we present an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWaT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors. However, multi-source analysis is more robust because it can detect anomalies from multiple sources simultaneously, while achieving comparable precision for each of the sources.",
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"affiliation": "Queensland University of Technology (QUT)",
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"abstract": "Voronoi diagrams are powerful for solving spatial problems among particles with different sizes. In particular, the Voronoi diagram of spheres, also called the additively-weighted Voronoi diagram, has proven its powerful capability for solving molecular biology/material science problems in an unified framework. Beta-complex is a generalization of (weighted) alpha-complex and they share similarities and dissimilarities. This paper presents the Beta Concept program which facilitates easy understanding of the powerful capability of the Voronoi diagram, its dual structure, and the beta-complex in the plane. Beta Concept is a Windows program with a graphic user interface written in the standard C++ language and is freely available at the Voronoi Diagram Research Center (http://voronoi.hanyang.ac.kr).",
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"abstract": "T cells play a critical role in the adaptive immune response. They perform their function by recognizing infected cells presenting peptides on a specialized complex known as the MHC. The recognition process involves binding of the peptide-loaded MHC to the T cell receptor (TCR), a surface molecule comprised of an alpha and a beta chain. A large body of evidence suggests that T cells can respond to previously unseen pathogens, a phenomenon known as cross-reactivity. Cross-reactivity has important medical implications, as cross-reactive responses can be either protective or lead to disease. A possible mechanism that has been proposed to explain cross-reactivity is the differential usage of the alpha and beta chains, whereas one peptide can be recognized predominantly by the alpha chain and a different peptide by the beta chain. In this study we carry out a systematic analysis of a non-redundant set of 67 crystal structures, measuring TCR alpha/beta usage and its relationship with structural features of the interaction. Our results show a wide range of TCR alpha/beta usage in different complexes. Further, we find that alpha/beta usage significantly correlates with one of the docking angles between the TCR and the MHC.",
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"content": "T cells play a critical role in the adaptive immune response. They perform their function by recognizing infected cells presenting peptides on a specialized complex known as the MHC. The recognition process involves binding of the peptide-loaded MHC to the T cell receptor (TCR), a surface molecule comprised of an alpha and a beta chain. A large body of evidence suggests that T cells can respond to previously unseen pathogens, a phenomenon known as cross-reactivity. Cross-reactivity has important medical implications, as cross-reactive responses can be either protective or lead to disease. A possible mechanism that has been proposed to explain cross-reactivity is the differential usage of the alpha and beta chains, whereas one peptide can be recognized predominantly by the alpha chain and a different peptide by the beta chain. In this study we carry out a systematic analysis of a non-redundant set of 67 crystal structures, measuring TCR alpha/beta usage and its relationship with structural features of the interaction. Our results show a wide range of TCR alpha/beta usage in different complexes. Further, we find that alpha/beta usage significantly correlates with one of the docking angles between the TCR and the MHC.",
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"abstract": "The response characteristics of the alpha-beta filter are used to quantify the filter's performance against different measurement models representing a target's trajectory. The transfer functions for an alpha-beta filter are used to derive closed form (solutions) expressions for smoothed position and velocity outputs for various measurement models. The filter's response to constant velocity targets is found to be the input plus a sinusoidal transient. Constant acceleration measurement models, in addition, yield a steady state bias that is a function of the filter parameters alpha and beta . Finally, the filter's response to a sinusoidal input is determined.<>",
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"abstract": "The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions such as the grey-world and white patch assumptions. In this paper, 3D geometry models are used to determine which color constancy method to use for the different geometrical regions found in images. To this end, images are first classified into stages (rough 3D geometry models). According to the stage models, images are divided into different regions using hard and soft segmentation. After that, the best color constancy algorithm is selected for each geometry segment. As a result, light source estimation is tuned to the global scene geometry. Our algorithm opens the possibility to estimate the remote scene illumination color, by distinguishing nearby light source from distant illuminants. Experiments on large scale image datasets show that the proposed algorithm outperforms state-of-the-art single color constancy algorithms with an improvement of almost 14% of median angular error. When using an ideal classifier (i.e, all of the test images are correctly classified into stages), the performance of the proposed method achieves an improvement of 31% of median angular error compared to the best-performing single color constancy algorithm.",
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"abstract": "In order to provide high data rate over wireless channels and improve the system capacity, Multiple-Input Multiple-Output (MIMO) wireless communication systems exploit spatial diversity by using multiple transmit and receive antennas. Moreover, to achieve high date rate and fulfill the power, MIMO systems are equipped with High Power Amplifiers (HPAs). However, HPAs cause nonlinear distortions and affect the receiver's performance. In this paper, we investigate the joint effects of HPA nonlinearity and frequency selective channel on the performance of MIMO receiver. Then, we propose two equalization schemes to compensate simultaneously nonlinear distortions and frequency selective channel effects. The first one is based on a feedforward Neural Network (NN) named (NN-MIMO-Receiver) and the second uses NN technique and LMS equalizer (LMS-NN-MIMO). The Levenberg-Marquardt algorithm (LM) is used for neural network training, which has proven [1] to exhibit a very good performance with lower computation complexity and faster convergence than other algorithms used in literature. These proposed methods are compared in term of Symbol Error Rate (SER) running under nonlinear frequency selective channel.",
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"affiliation": "SUPCOM, Innov'Com Laboratory, Carthage University, Tunis, Tunisia",
"fullName": "Oussama B. Belkacem",
"givenName": "Oussama B.",
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"affiliation": "SUPCOM, Innov'Com Laboratory, Carthage University, Tunis, Tunisia",
"fullName": "Rafik Zayani",
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"surname": "Zayani",
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"affiliation": "SUPCOM, Innov'Com Laboratory, Carthage University, Tunis, Tunisia",
"fullName": "Mohamed L. Ammari",
"givenName": "Mohamed L.",
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"fullName": "Daniel Roviras",
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"abstract": "The structures of the four representative dynamic neural networks (NN) are presented. In order to compare the performance of different dynamic NN in the nonlinear system identification, they are used for identification of the same nonlinear dynamic system, using the genetic algorithm (GA) to train the weights of the Elman net, the modified Elman net, internal time-delayed recurrent NN and time-delayed NN. The simulation results show the generalization ability of the four dynamic NN and provide the high precision of model of the nonlinear dynamic system. It illustrates the advantages and disadvantages of the different dynamic NN.",
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"title": "Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks",
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"abstract": "Diffeomorphic deformable image registration is crucial in many medical image studies, as it offers unique, special features including topology preservation and invertibility of the transformation. Recent deep learning-based deformable image registration methods achieve fast image registration by leveraging a convolutional neural network (CNN) to learn the spatial transformation from the synthetic ground truth or the similarity metric. However, these approaches often ignore the topology preservation of the transformation and the smoothness of the transformation which is enforced by a global smoothing energy function alone. Moreover, deep learning-based approaches often estimate the displacement field directly, which cannot guarantee the existence of the inverse transformation. In this paper, we present a novel, efficient unsupervised symmetric image registration method which maximizes the similarity between images within the space of diffeomorphic maps and estimates both forward and inverse transformations simultaneously. We evaluate our method on 3D image registration with a large scale brain image dataset. Our method achieves state-of-the-art registration accuracy and running time while maintaining desirable diffeomorphic properties.",
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"content": "Diffeomorphic deformable image registration is crucial in many medical image studies, as it offers unique, special features including topology preservation and invertibility of the transformation. Recent deep learning-based deformable image registration methods achieve fast image registration by leveraging a convolutional neural network (CNN) to learn the spatial transformation from the synthetic ground truth or the similarity metric. However, these approaches often ignore the topology preservation of the transformation and the smoothness of the transformation which is enforced by a global smoothing energy function alone. Moreover, deep learning-based approaches often estimate the displacement field directly, which cannot guarantee the existence of the inverse transformation. In this paper, we present a novel, efficient unsupervised symmetric image registration method which maximizes the similarity between images within the space of diffeomorphic maps and estimates both forward and inverse transformations simultaneously. We evaluate our method on 3D image registration with a large scale brain image dataset. Our method achieves state-of-the-art registration accuracy and running time while maintaining desirable diffeomorphic properties.",
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"normalizedAbstract": "Diffeomorphic deformable image registration is crucial in many medical image studies, as it offers unique, special features including topology preservation and invertibility of the transformation. Recent deep learning-based deformable image registration methods achieve fast image registration by leveraging a convolutional neural network (CNN) to learn the spatial transformation from the synthetic ground truth or the similarity metric. However, these approaches often ignore the topology preservation of the transformation and the smoothness of the transformation which is enforced by a global smoothing energy function alone. Moreover, deep learning-based approaches often estimate the displacement field directly, which cannot guarantee the existence of the inverse transformation. In this paper, we present a novel, efficient unsupervised symmetric image registration method which maximizes the similarity between images within the space of diffeomorphic maps and estimates both forward and inverse transformations simultaneously. We evaluate our method on 3D image registration with a large scale brain image dataset. Our method achieves state-of-the-art registration accuracy and running time while maintaining desirable diffeomorphic properties.",
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"Deformation",
"Image Registration",
"Learning Artificial Intelligence",
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"Neural Nets",
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"Convolutional Neural Network",
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"Special Features Including Topology Preservation",
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"Global Smoothing Energy Function",
"Deep Learning Based Approaches",
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"Topology",
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"Convolutional Neural Networks",
"Brain"
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"affiliation": "Department of Computer Science and Engineering, The Hong Kong University of Science and Technology",
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"affiliation": "Department of Computer Science and Engineering, The Hong Kong University of Science and Technology",
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