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"title": "Aesthetics and expanding perception in fluid physics",
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"abstract": "Flow Visualization is both the practice of making fluid physics visible and a course that focuses on the practice of making those images, as short video and stills. Since 2003, this course has been cross-listed as a mechanical engineering elective and a fine arts studio course, and brings together mixed teams of engineering and fine arts photography or film students. With its focus on the production of aesthetically pleasing and scientifically useful images of fluid flows, it is one of the few course offerings that explicitly calls upon engineering students to use an aesthetic sense in their studies. In prior work, this course was associated with a positive shift in affect toward fluid flows, which we measured through the Fluids Perception Survey (FluPerS) [1]. In the same survey, students expressed a greater awareness of fluids. This outcome was in contrast to the survey results from Fluid Mechanics, a traditional engineering core course, with a highly analytic, mathematical approach. Exit surveys of students in Fluid Mechanics reveal a negative shift in affect, which is typical of other technical courses, with no comments about awareness of fluids. The increased awareness of fluids can be termed an expansion of perception — when learners see everyday objects, events, or issues through the lens of the content [2]. Expansion of perception is often associated with deeper conceptual understanding and the ability to transfer learning to new settings. Building on the prior survey work, we conducted interviews with students from Flow Vis, both engineering students (n=4) and art/film students (n=5), in addition to Fluid Mechanics students (n=3), at the beginning and end of a semester. Their comments, along with open responses in the survey (n=86), are being analyzed using an iterative process to develop an emergent coding scheme. We want to discover what about Flow Vis helps students, both from engineering and art, internalize and apply their understanding of fluids. What role does the emphasis on aesthetics, so familiar to the art students, so uncommon in coursework for the engineers, play? Finally, how can we adapt the teaching practices from Flow Vis for other courses, other content areas? Analysis of data and future course suggestions are discussed.",
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"affiliation": "Department of Mechanical Engineering, University of Colorado Boulder, CO, USA, 80309",
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"affiliation": "Department of Physics, University of Colorado Boulder, CO, USA, 80309",
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"abstract": "In this paper, we study the problem of mesh denoising for improving the single pass surface estimation on normals and curvature tensors. We focus mainly on the engineering objects represented as dense triangle meshes. In particular, a two run non-linear diffusion algorithm based on optimal estimation theory is proposed to adaptively filter out the undesired discontinuities introduced by noise while preserving the underlying features. We show that the proposed filter can successfully improve the local surface estimates while preserving the desired features in terms of tangential and curvature discontinuities.",
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"affiliation": "Purdue University",
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"affiliation": "Purdue University",
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"abstract": "In this paper, we propose an holistic, fully automatic approach to 3D Facial Expression Recognition (FER). A novel facial representation, namely Differential Mean Curvature Maps (DMCMs), is proposed to capture both global and local facial surface deformations which typically occur during facial expressions. These DMCMs are directly extracted from 3D depth images, by calculating the mean curvatures thanks to an integral computation. To account for facial morphology variations, they are further normalized through an aspect ratio deformation. Finally, Histograms of Oriented Gradients (HOG) are applied to regions of these normalized DMCMs and allow for the generation of facial features that can be fed to the widely used Multiclass-SVM classification algorithm. Using the protocol proposed by Gong et al. [1] on the BU-3DFE dataset, the proposed approach displays competitive performance while staying entirely automatic.",
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"abstract": "Massive routinely-acquired raw volumetric datasets are hard to be deeply exploited by cyber worlds related downstream applications due to the challenges in accurate and efficient shape modeling. This paper systematically advocates an interactive 3D shape modeling framework for raw volumetric datasets by iteratively optimizing Hessian-constrained local implicit surfaces. The key idea is to incorporate contour based interactive segmentation into the generalized local implicit surface reconstruction. Our framework allows a user to flexibly define derivative constraints up to the second order via intuitively placing contours on the cross sections of volumetric images and fine-tuning the eigenvector frame of Hessian matrix. It enables detail-preserving local implicit representation while combating certain difficulties due to ambiguous image regions, low-quality irregular data, close sheets, and massive coefficients involved extra computing burden. Moreover, we conduct extensive experiments on some volumetric images with blurry object boundaries, and make comprehensive, quantitative performance evaluation between our method and the state-of-the-art radial basis function based techniques. All the results demonstrate our method's advantages in the accuracy, detail-preserving, efficiency, and versatility of shape modeling.",
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"content": "In dynamic fluid simulation, the user often need to control the local regions of the entire fluid scene for the special design effects, such as flowing marks and icons in dynamic fluids. Currently, the research aimed at such an issue is rare. It is rather difficult to realize the local feature effects using the global control methods. This paper proposes a novel local-control method for dynamic fluid simulation. Specifically, our method enables the generation vivid local features according to the pattern image specified by the user. We first search the whole fluid space to find the local areas best matched to the input pattern image. The special driving force is calculated to shape the local areas into the target pattern. In order to ensure the continuity of the whole velocity field, we mix the velocity fields of the local area and the one of its corresponding position in the global zone. Our method can provide the user or designers with the local-control tool for editing the dynamic fluid scenes intuitively. The dynamic fluid simulations with the local features have been experimented, which showed the validity and effectiveness of our method.",
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"title": "Crowdsourcing audience perspectives on classical music",
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"abstract": "Program notes of (Western) classical music are usually written by musical experts. As a consequence, program notes information may not be very accessible to audiences which are less familiar with this music genre. To gain more insight in the way in which ‘uninitiated’ audience perceives and describes symphonic concert performances, in this work, we describe the acquisition of a dataset of various forms of timeline commentary through crowdsourcing mechanisms. As we show, different audience categories use different vocabularies, and consider different anchors on the timeline to be important.",
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"abstract": "Classical music is associated with a set of social and cognitive barriers that prevent potentially interested novice classical music listeners from attending concerts. The possible strategies to overcome these barriers and make the genre appealing to new audiences are diverse. In this paper, we present \\textit{Becoming the Maestro}, a game where users impersonate the conductor of an orchestra during a concert in a way that allows them to better understand musical concepts of symphonic music. A user study of the game prototype with 20 subjects proved that the game succeeded in increasing curiosity for classical music.",
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"abstract": "Although light field data provides abundant cues for depth estimation, light field depth estimation suffers from occlusion and uncertain edges. In this paper, we propose occlusion robust light field depth estimation using segmentation guided bilateral filtering. First, we calculate refocused images from light field data using digital refocusing. Second, we perform support vector machines (SVM) classification to classify occluded pixels and non-occluded pixels. Third, we conduct different cost estimations on occluded and non-occluded pixels, and remove noise by cost volume filtering. Finally, we perform segmentation-guided bilateral filtering to refine the depth map while preserving edges. Experimental results on both synthetic and our own data sets demonstrate that the proposed method achieves light field depth estimation robust to occlusion while successfully preserving edges.",
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"content": "Although light field data provides abundant cues for depth estimation, light field depth estimation suffers from occlusion and uncertain edges. In this paper, we propose occlusion robust light field depth estimation using segmentation guided bilateral filtering. First, we calculate refocused images from light field data using digital refocusing. Second, we perform support vector machines (SVM) classification to classify occluded pixels and non-occluded pixels. Third, we conduct different cost estimations on occluded and non-occluded pixels, and remove noise by cost volume filtering. Finally, we perform segmentation-guided bilateral filtering to refine the depth map while preserving edges. Experimental results on both synthetic and our own data sets demonstrate that the proposed method achieves light field depth estimation robust to occlusion while successfully preserving edges.",
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"abstract": "This paper presents an efficient keyframeless image-based rendering technique. An intermediate image is used to exploit the coherences among neighboring frames. The pixels in the intermediate image are first rendered by a ray-casting method and then warped to the intermediate image at the current viewpoint and view direction. We use an offset buffer to record the precise positions of these pixels in the intermediate image. Every frame is generated in three steps: warping the intermediate image onto the frame, filling in holes, and selectively rendering a group of \"old\" pixels. By dynamically adjusting the number of those \"old\" pixels in the last step, the workload at every frame can be balanced. The pixels generated by the last two steps make contributions to the new intermediate image. Unlike occasional keyframes in conventional image-based rendering which need to be totally rerendered, intermediate images only need to be partially updated at every frame. In this way, we guarantee more stable frame rates and more uniform image qualities. The intermediate image can be warped efficiently by a modified incremental 3D warp algorithm. As a specific application, we demonstrate our technique with a voxel-based terrain rendering system.",
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"content": "This paper presents an efficient keyframeless image-based rendering technique. An intermediate image is used to exploit the coherences among neighboring frames. The pixels in the intermediate image are first rendered by a ray-casting method and then warped to the intermediate image at the current viewpoint and view direction. We use an offset buffer to record the precise positions of these pixels in the intermediate image. Every frame is generated in three steps: warping the intermediate image onto the frame, filling in holes, and selectively rendering a group of \"old\" pixels. By dynamically adjusting the number of those \"old\" pixels in the last step, the workload at every frame can be balanced. The pixels generated by the last two steps make contributions to the new intermediate image. Unlike occasional keyframes in conventional image-based rendering which need to be totally rerendered, intermediate images only need to be partially updated at every frame. In this way, we guarantee more stable frame rates and more uniform image qualities. The intermediate image can be warped efficiently by a modified incremental 3D warp algorithm. As a specific application, we demonstrate our technique with a voxel-based terrain rendering system.",
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"abstract": "In the process of reconstruction of traffic accident, it is very necessary to have a analysis of human body's biomechanics simulation. Starting from the traffic case, the injury caused by the airbag to the human head is discovered. Establish the finite element model of airbag and use the human head model established by team to have a simulation and analyze. The simulation results are basically consistent with the real head injury, and verified the speed of the car and the degree of head injury. The important role of the human body biomechanics research in traffic accident reconstruction and its application are expounded.",
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"abstract": "Several video-based 3D pose and shape estimation algorithms have been proposed to resolve the temporal inconsistency of single-image-based methods. However it still remains challenging to have stable and accurate reconstruction. In this paper, we propose a new framework Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation (DTS-VIBE), to generate 3D human pose and mesh from RGB videos. We reformulate the task as a multi-modality problem that fuses RGB and optical flow for more reliable estimation. In order to fully utilize both sensory modalities (RGB or optical flow), we train a two-stream temporal network based on transformer to predict SMPL parameters. The supplementary modality, optical flow, helps to maintain temporal consistency by leveraging motion knowledge between two consecutive frames. The proposed algorithm is extensively evaluated on the Human3.6 and 3DPW datasets. The experimental results show that it outperforms other state-of-the-art methods by a significant margin.",
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"content": "Several video-based 3D pose and shape estimation algorithms have been proposed to resolve the temporal inconsistency of single-image-based methods. However it still remains challenging to have stable and accurate reconstruction. In this paper, we propose a new framework Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation (DTS-VIBE), to generate 3D human pose and mesh from RGB videos. We reformulate the task as a multi-modality problem that fuses RGB and optical flow for more reliable estimation. In order to fully utilize both sensory modalities (RGB or optical flow), we train a two-stream temporal network based on transformer to predict SMPL parameters. The supplementary modality, optical flow, helps to maintain temporal consistency by leveraging motion knowledge between two consecutive frames. The proposed algorithm is extensively evaluated on the Human3.6 and 3DPW datasets. The experimental results show that it outperforms other state-of-the-art methods by a significant margin.",
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"abstract": "In this paper we present a method of human body reshaping which is based on 2D images. We first reshape a 3D human body from a 2D image by changing the 3D morphable circles in the 3D human body model, and then project the 3D model onto the original 2D plane. There are three main steps in our method. First, we can acquire the human body contour and the limbs information through the marks which were made by users. Secondly, we generate the 3D morphable circles from the body contour. The 3D morphable circle is a set of 3D annular points, and we can connect the points in each circle to create 3D meshes which can be used to generate the 3D human model. At last, we use texture mapping and affine transformation to reshape the body model and project it on to the 2D plan to implement the 2D images deformation algorithm. The method proposed in this paper can effectively combine 2D images with 3D graphics with little costs. The method is easy-to-use and has a definite application prospect in e-commerce and online entertainment fields. A lot of experiments illustrate that our method is acceptable.",
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"abstract": "This paper describes a novel deep neural network architecture to reconstruct an accurate human body shape from a single depth map. The proposed method utilizes a statistical parametric body shape model, which represents a wide variety of body shape with low-dimensional body shape parameters. We formulate the body shape reconstruction as a regression problem of the body shape parameters. One of the biggest challenges of the single-image shape reconstruction lies in a gap between input and output modalities. This is because an input depth map only contains a surface of a human body, while the output is a full 3D body shape model. To bridge this gap, we utilize dedicated two deep neural networks ShapeEncoder and DepthMapEncoder, which respectively process the 3D body model and the depth map. These two networks are bridged with a learned latent body feature space to enable accurate single-image body shape estimation. Furthermore, the proposed method also uses body joint positions estimated from the depth map to further improve the performance. The proposed approach is evaluated on real depth maps taken from 30 subjects and achieves significant performance improvements over the existing methods. Contribution-Multiple deep neural networks form a novel feature bridging architecture to achieve significant performance improvements.",
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"abstract": "The pathogenesis of atrial fibrillation (AF) is closely related to the fibrotic tissues in left atrial (LA). Delay-enhancement magnetic resonance imaging (DE-MRI) has been widely used in the ablation of atrial fibrillation, which can accurately describe the distribution of myocardial fibrosis and postoperative scars. Combining EM algorithm, level-set and graph-cut, this paper proposes a method to segment the fibrotic tissues and postoperative scars and also quantify their proportion in left atrial from DE-MRI. In 4 clinical cases, our method can accomplish the extraction of heart, the segmentation of left atrium and sequentially the quantification of the fibrotic tissues nearby with little manual intervention. Experimental results show that accurate segmentation of LA is achieved in 55 slices with 96 slices containing LA among 4 cases in total. With manual correction in the rest slices, the final results about the proportion of fibrotic tissues in LA are 14.78%, 21.02%, 25.17%, 14.77% respectively which are consistent with the clinical diagnosis. Evaluated by the clinician, our method is robust against different resolution and can provide auxiliary function for ablation of AF.",
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"content": "The pathogenesis of atrial fibrillation (AF) is closely related to the fibrotic tissues in left atrial (LA). Delay-enhancement magnetic resonance imaging (DE-MRI) has been widely used in the ablation of atrial fibrillation, which can accurately describe the distribution of myocardial fibrosis and postoperative scars. Combining EM algorithm, level-set and graph-cut, this paper proposes a method to segment the fibrotic tissues and postoperative scars and also quantify their proportion in left atrial from DE-MRI. In 4 clinical cases, our method can accomplish the extraction of heart, the segmentation of left atrium and sequentially the quantification of the fibrotic tissues nearby with little manual intervention. Experimental results show that accurate segmentation of LA is achieved in 55 slices with 96 slices containing LA among 4 cases in total. With manual correction in the rest slices, the final results about the proportion of fibrotic tissues in LA are 14.78%, 21.02%, 25.17%, 14.77% respectively which are consistent with the clinical diagnosis. Evaluated by the clinician, our method is robust against different resolution and can provide auxiliary function for ablation of AF.",
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"title": "Proceedings Computers in Cardiology",
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"title": "Left ventricular function from computer processed magnetic resonance images",
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"abstract": "Cross-sectional magnetic resonance (MR) images of the heart from nine male and ten female normal subjects were computer processed to establish normal templates of left ventricular function. Ventricular volumes at end-diastole and end-systole were significantly greater in males than females but ejection fractions were identical. For analysis of regional endocardial motion, the use of a floating centroid gave more uniform values compared with a fixed centroid method. Endocardial motion values were not significantly different for males and females. The data obtained can be used to define abnormalities of left ventricular function in patients with heart disease. Male/female differences in ventricular volume will require separate templates but it appears that analysis of ejection fraction and regional endocardial motion can be performed from the same templates for males and females.<>",
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"content": "Cross-sectional magnetic resonance (MR) images of the heart from nine male and ten female normal subjects were computer processed to establish normal templates of left ventricular function. Ventricular volumes at end-diastole and end-systole were significantly greater in males than females but ejection fractions were identical. For analysis of regional endocardial motion, the use of a floating centroid gave more uniform values compared with a fixed centroid method. Endocardial motion values were not significantly different for males and females. The data obtained can be used to define abnormalities of left ventricular function in patients with heart disease. Male/female differences in ventricular volume will require separate templates but it appears that analysis of ejection fraction and regional endocardial motion can be performed from the same templates for males and females.<>",
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"Cardiology",
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"abstract": "Deep learning networks have obtained promising results for left ventricle segmentation in magnetic resonance images. Training with datasets containing low diversity of examples impacts the generalization ability of these networks, which is a problem in the medical image context. Strategies such as data augmentation and post-processing techniques have been applied to improve learning and generalization, but the influence of pre-processing methods is still underexplored. This work aims to evaluate the impact of pre-processing steps on the generalization of deep learning networks for left ventricle segmentation. Experiments were conducted with public datasets considering different configurations of pre-processing steps. We also compare a novel approach for automatic region of interest (ROI) detection in relation to manual approaches. The results suggest that pre-processing procedures favor the generalization, even when other strategies are not present. New methods to detect the ROI with size invariability are promising to obtain adequate generalization ability in deep learning networks.",
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"content": "Deep learning networks have obtained promising results for left ventricle segmentation in magnetic resonance images. Training with datasets containing low diversity of examples impacts the generalization ability of these networks, which is a problem in the medical image context. Strategies such as data augmentation and post-processing techniques have been applied to improve learning and generalization, but the influence of pre-processing methods is still underexplored. This work aims to evaluate the impact of pre-processing steps on the generalization of deep learning networks for left ventricle segmentation. Experiments were conducted with public datasets considering different configurations of pre-processing steps. We also compare a novel approach for automatic region of interest (ROI) detection in relation to manual approaches. The results suggest that pre-processing procedures favor the generalization, even when other strategies are not present. New methods to detect the ROI with size invariability are promising to obtain adequate generalization ability in deep learning networks.",
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"content": "The cardiac is one of the essential organs, and the segmentation of the left and right ventricular of cardiac is essential in diagnosing various heart diseases. The most popular method for the segmentation of 3D MRI images is the nnUNet. However, the 3D MRI volume of the ventricular contains other organs which interfere with the segmentation of the ventricular. Hence, we proposed a novel region-aware U-Net segmentation method RegUNet for ventricular segmentation. RegUNet improves the ventricular's segmentation performance by first capturing the region of interest (RoI) of the ventricular and then segmenting the ventricular with the captured RoI features, which reduces the segmentation module's difficulty by keeping the cardiac's features and leaving others such that RegUNet can focus on ventricular segmentation. Besides, since the model segments the ventricular with the captured RoI features, it saves the model's computing resources from identifying the background of the volume. Since 3D cardiac MRI volumes scanned by the different devices have diverse statistical characteristics, which causes the model's performance in processing the multi-source cardiac volumes to be unstable. We stabilize the model's performance with a multi-sources feature normalization strategy, which normalizes the feature from a different source with different parameters. We validated the proposed method on the M&MS dataset, a multi-sources 3D MRI cardiac segmentation dataset. Experiments showed that RegUNet's segmentation ability reached the state-of-the-art.",
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"normalizedAbstract": "The cardiac is one of the essential organs, and the segmentation of the left and right ventricular of cardiac is essential in diagnosing various heart diseases. The most popular method for the segmentation of 3D MRI images is the nnUNet. However, the 3D MRI volume of the ventricular contains other organs which interfere with the segmentation of the ventricular. Hence, we proposed a novel region-aware U-Net segmentation method RegUNet for ventricular segmentation. RegUNet improves the ventricular's segmentation performance by first capturing the region of interest (RoI) of the ventricular and then segmenting the ventricular with the captured RoI features, which reduces the segmentation module's difficulty by keeping the cardiac's features and leaving others such that RegUNet can focus on ventricular segmentation. Besides, since the model segments the ventricular with the captured RoI features, it saves the model's computing resources from identifying the background of the volume. Since 3D cardiac MRI volumes scanned by the different devices have diverse statistical characteristics, which causes the model's performance in processing the multi-source cardiac volumes to be unstable. We stabilize the model's performance with a multi-sources feature normalization strategy, which normalizes the feature from a different source with different parameters. We validated the proposed method on the M&MS dataset, a multi-sources 3D MRI cardiac segmentation dataset. Experiments showed that RegUNet's segmentation ability reached the state-of-the-art.",
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"Biomedical MRI",
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"affiliation": "College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China",
"fullName": "Xiaoting Huang",
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"affiliation": "College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China",
"fullName": "Wenjie Chen",
"givenName": "Wenjie",
"surname": "Chen",
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{
"affiliation": "College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China",
"fullName": "Xueting Liu",
"givenName": "Xueting",
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{
"affiliation": "College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China",
"fullName": "Huisi Wu",
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{
"affiliation": "College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China",
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{
"affiliation": "College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China",
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"abstract": "Although those deep learning networks have achieved remarkable performance in various biomedical image segmentation tasks, they rely on massive labeled data for training, which is time-consuming to acquire. To alleviate this challenging issue, semi-supervised learning (SSL) has shown the potential to simultaneously leverage between abundant unlabeled samples and limited labeled data. In this work, we propose a new SSL approach for left atrial and liver tumor segmentation called DTSC-Net. First, two teacher networks and one student network are optimized together through a supervised loss and two unsupervised consistency losses. Furthermore, pseudo labels are yielded by using the first teacher network on unlabeled data. In addition, the weights of the second teacher network are an exponential moving average of the student network’s weights. Moreover, the student network learns from the two teacher networks by minimizing a supervised segmentation loss, a pseudo-labeling unsupervised consistency (PUC) loss, and a dual-teacher unsupervised consistency (DUC) loss with respect to the targets of the two teacher networks. Experiment results show that our approach achieves state-of-the-art semi-supervised segmentation performance on the LA2018 and LiTS2017 datasets.",
"abstracts": [
{
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"content": "Although those deep learning networks have achieved remarkable performance in various biomedical image segmentation tasks, they rely on massive labeled data for training, which is time-consuming to acquire. To alleviate this challenging issue, semi-supervised learning (SSL) has shown the potential to simultaneously leverage between abundant unlabeled samples and limited labeled data. In this work, we propose a new SSL approach for left atrial and liver tumor segmentation called DTSC-Net. First, two teacher networks and one student network are optimized together through a supervised loss and two unsupervised consistency losses. Furthermore, pseudo labels are yielded by using the first teacher network on unlabeled data. In addition, the weights of the second teacher network are an exponential moving average of the student network’s weights. Moreover, the student network learns from the two teacher networks by minimizing a supervised segmentation loss, a pseudo-labeling unsupervised consistency (PUC) loss, and a dual-teacher unsupervised consistency (DUC) loss with respect to the targets of the two teacher networks. Experiment results show that our approach achieves state-of-the-art semi-supervised segmentation performance on the LA2018 and LiTS2017 datasets.",
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"normalizedAbstract": "Although those deep learning networks have achieved remarkable performance in various biomedical image segmentation tasks, they rely on massive labeled data for training, which is time-consuming to acquire. To alleviate this challenging issue, semi-supervised learning (SSL) has shown the potential to simultaneously leverage between abundant unlabeled samples and limited labeled data. In this work, we propose a new SSL approach for left atrial and liver tumor segmentation called DTSC-Net. First, two teacher networks and one student network are optimized together through a supervised loss and two unsupervised consistency losses. Furthermore, pseudo labels are yielded by using the first teacher network on unlabeled data. In addition, the weights of the second teacher network are an exponential moving average of the student network’s weights. Moreover, the student network learns from the two teacher networks by minimizing a supervised segmentation loss, a pseudo-labeling unsupervised consistency (PUC) loss, and a dual-teacher unsupervised consistency (DUC) loss with respect to the targets of the two teacher networks. Experiment results show that our approach achieves state-of-the-art semi-supervised segmentation performance on the LA2018 and LiTS2017 datasets.",
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"title": "PSPU-Net for Automatic Short Axis Cine MRI Segmentation of Left and Right Ventricles",
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"abstract": "Characterization of the heart anatomy and function is mostly done with magnetic resonance image cine series. To achieve a correct characterization, the volume of the right and left ventricle need to be segmented, which is a timeconsuming task. We propose a new convolutional neural network architecture that combines U-net with PSP modules (PSPU-net) for the segmentation of left and right ventricle cavities and left ventricle myocardium in the diastolic frame of short-axis cine MRI images and compare its results against a classic 3D U-net architecture. We used a dataset containing 399 cases in total. The results showed higher quality results in both segmentation and final volume estimation for a test set of 99 cases in the case of the PSPU-net, with global dice metrics of 0.910 and median absolute relative errors in volume estimations of 0.026 and 0.039 for the left ventricle cavity and myocardium and 0.051 for the right ventricles cavity.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Characterization of the heart anatomy and function is mostly done with magnetic resonance image cine series. To achieve a correct characterization, the volume of the right and left ventricle need to be segmented, which is a timeconsuming task. We propose a new convolutional neural network architecture that combines U-net with PSP modules (PSPU-net) for the segmentation of left and right ventricle cavities and left ventricle myocardium in the diastolic frame of short-axis cine MRI images and compare its results against a classic 3D U-net architecture. We used a dataset containing 399 cases in total. The results showed higher quality results in both segmentation and final volume estimation for a test set of 99 cases in the case of the PSPU-net, with global dice metrics of 0.910 and median absolute relative errors in volume estimations of 0.026 and 0.039 for the left ventricle cavity and myocardium and 0.051 for the right ventricles cavity.",
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"affiliation": "Universitat Politècnica de València,Center for Biomaterials and Tissue Engineering,Valencia,Spain",
"fullName": "Manuel Pérez-Pelegrí",
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"affiliation": "Unidad de Imagen Cardíaca ERESA-ASCIRES Grupo Biomédico,Valencia,Spain",
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"abstract": "A central open question in the study of non-uniform constraint satisfaction problems (CSPs) is the dichotomy conjecture of Feder and Vardi stating that the CSP over a fixed constraint language is either NP-complete, or tractable. One of the main achievements in this direction is a result of Bulatov (LICS'03) confirming the dichotomy conjecture for conservative CSPs, that is, CSPs over constraint languages containing all unary relations. Unfortunately, the proof is very long and complicated, and therefore hard to understand even for a specialist. This paper provides a short and transparent proof.",
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"content": "The relief models made by the common modeling software are difficult to be consistent with the digital terrain model (DTM) and cannot fully express their morphological characteristics (such as the form, the structure, the spatial distributions and so on). In view of the above question, this paper advances an automatic modeling method of 3D relief symbols with building-up style. A component library of 3D relief symbols will be built, which consists of three parts: the public component library, the private component library and the metadata database. Some components of the different 3D relief symbols will be encapsulated and put into the public component library if their models can be built by the similar or the same method during the modeling process, and the others will be encapsulated and put into the private component library. Each component in the public or private component library will be described by a metadata in the metadata database. All the models of 3D relief symbols can be built by some components in the component library. The method can be used to reduce the production cost and improve the production efficiency of 3D relief symbol.",
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"abstract": "With the rapid development of computer graphics and image technology and 3D modeling, great progress has been made in the field of 3D digital relief. Hollowed out relief is a form of concave relief, which is obtained by carving out the background plane on the basis of concave relief. Digital relief is generated by computer-aided modeling, which overcomes the shortcomings of traditional manual relief, such as low production efficiency and difficult to modify products, and is easy to save. In this paper, the design and implementation of image relief based on computer 3D modeling, the basic modeling process of computer 3D modeling, plane relief and the generation method of fractal pattern concave relief based on distance transformation are discussed. A differential algorithm is proposed to realize the concave convex effect of image relief. Experiments are carried out on different models on laptops configured as main frequency and graphic display card to verify the rationality of the above algorithm, The time consumption of viewpoint selection of different models is tested, and the modeling efficiency of different input models is counted. The test results show that without program optimization, the viewpoint selection time of the model with the number of mesh patches of 15K ~ 50K ranges from 14 to 58s, among which the development of model visibility judgment and significance calculation is the largest, accounting for about 50% ~ 60% of the running time of the whole process. Therefore, the algorithm optimization of visibility judgment will help to improve the operation efficiency of the system and reduce the waiting time of users. Under the same input model, the computational efficiency of this algorithm is hundreds of times higher than that of the literature method. For the models in the table, due to the complex occlusion degree of patches, the modeling time of shallow relief is up to 3.778 seconds; For the mesh model with the number of patches below 35K, the efficiency of bas relief modeling is about, which basically meets the requirements of real-time.",
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"normalizedAbstract": "With the rapid development of computer graphics and image technology and 3D modeling, great progress has been made in the field of 3D digital relief. Hollowed out relief is a form of concave relief, which is obtained by carving out the background plane on the basis of concave relief. Digital relief is generated by computer-aided modeling, which overcomes the shortcomings of traditional manual relief, such as low production efficiency and difficult to modify products, and is easy to save. In this paper, the design and implementation of image relief based on computer 3D modeling, the basic modeling process of computer 3D modeling, plane relief and the generation method of fractal pattern concave relief based on distance transformation are discussed. A differential algorithm is proposed to realize the concave convex effect of image relief. Experiments are carried out on different models on laptops configured as main frequency and graphic display card to verify the rationality of the above algorithm, The time consumption of viewpoint selection of different models is tested, and the modeling efficiency of different input models is counted. The test results show that without program optimization, the viewpoint selection time of the model with the number of mesh patches of 15K ~ 50K ranges from 14 to 58s, among which the development of model visibility judgment and significance calculation is the largest, accounting for about 50% ~ 60% of the running time of the whole process. Therefore, the algorithm optimization of visibility judgment will help to improve the operation efficiency of the system and reduce the waiting time of users. Under the same input model, the computational efficiency of this algorithm is hundreds of times higher than that of the literature method. For the models in the table, due to the complex occlusion degree of patches, the modeling time of shallow relief is up to 3.778 seconds; For the mesh model with the number of patches below 35K, the efficiency of bas relief modeling is about, which basically meets the requirements of real-time.",
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"abstract": "In this paper, we present a learning-based approach for recovering the 3D geometry of human head from a single portrait image. Our method is learned in an unsupervised manner without any ground-truth 3D data. We represent the head geometry with a parametric 3D face model together with a depth map for other head regions including hair and ear. A two-step geometry learning scheme is proposed to learn 3D head reconstruction from in-the-wild face images, where we first learn face shape on single images using self-reconstruction and then learn hair and ear geometry using pairs of images in a stereo-matching fashion. The second step is based on the output of the first to not only improve the accuracy but also ensure the consistency of overall head geometry. We evaluate the accuracy of our method both in 3D and with pose manipulation tasks on 2D images. We alter pose based on the recovered geometry and apply a refinement network trained with adversarial learning to ameliorate the reprojected images and translate them to the real image domain. Extensive evaluations and comparison with previous methods show that our new method can produce high-fidelity 3D head geometry and head pose manipulation results.",
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"content": "In this paper, we present a learning-based approach for recovering the 3D geometry of human head from a single portrait image. Our method is learned in an unsupervised manner without any ground-truth 3D data. We represent the head geometry with a parametric 3D face model together with a depth map for other head regions including hair and ear. A two-step geometry learning scheme is proposed to learn 3D head reconstruction from in-the-wild face images, where we first learn face shape on single images using self-reconstruction and then learn hair and ear geometry using pairs of images in a stereo-matching fashion. The second step is based on the output of the first to not only improve the accuracy but also ensure the consistency of overall head geometry. We evaluate the accuracy of our method both in 3D and with pose manipulation tasks on 2D images. We alter pose based on the recovered geometry and apply a refinement network trained with adversarial learning to ameliorate the reprojected images and translate them to the real image domain. Extensive evaluations and comparison with previous methods show that our new method can produce high-fidelity 3D head geometry and head pose manipulation results.",
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"affiliation": "Beijing Institute of Technology",
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"affiliation": "Tsinghua University",
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