Datasets:
File size: 9,924 Bytes
f5985d0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | {
"created": "2025-03-19T15:54:57.477513+00:00",
"modified": "2025-03-19T15:54:57.783820+00:00",
"id": 15050523,
"conceptrecid": "10940193",
"doi": "10.5281/zenodo.15050523",
"conceptdoi": "10.5281/zenodo.10940193",
"doi_url": "https://doi.org/10.5281/zenodo.15050523",
"metadata": {
"title": "Melanoma Histopathology Dataset with Tissue and Nuclei Annotations",
"doi": "10.5281/zenodo.15050523",
"publication_date": "2025-03-19",
"description": "<h2><strong>Description</strong>:</h2>\n<p>This dataset is designed for development of deep learning models for segmentation of nuclei and tissue in melanoma H&E stained histopathology. Existing nuclei segmentation models that are trained on non-melanoma specific datasets have low performance due to the ability of melanocytes to mimic other cell types, whereas existing melanoma specific models utilize older, sub-optimal techniques. Moreover, these models do not provide tissue annotations necessary for determining the localization of tumor-infiltrating lymphocytes, which may hold value for predictive and prognostic tasks. To address this, we created a melanoma specific dataset with nuclei and tissue annotations. <br><br></p>\n<h2><strong>Methodology</strong>:</h2>\n<p><strong>Sample Collection</strong>:</p>\n<p>Regions of interest (ROIs) were sampled from H&E stained slides of 103 primary melanoma specimens and 102 metastatic melanoma specimens, scanned using a Hamamatsu scanner at 40× magnification (0.23 μm per pixel). All slides were obtained from regular diagnostic procedures.<br>From each specimen, a 40× magnified ROI of 1024×1024 pixels was selected for annotation. Additionally, a context ROI of 5120×5120 pixels was sampled to provide information about the broader context for the annotation process. Selection was performed by a trained medical expert (M.S.) and subsequently verified by a dermatopathologist (W.B.). Manual ROI selection ensured the inclusion of diverse tissue and nuclei types.</p>\n<p><strong>Annotation Process</strong>:</p>\n<ul>\n<li><strong>Nuclei segmentation<br></strong>Nuclei segmentations were generated using Hover-Net pretrained on the PanNuke dataset. Manual annotation adjustments were performed by author M.S. using QuPath, with the following nuclei categories: tumor, stroma, vascular endothelium, histiocyte, melanophage, lymphocyte, plasma cell, neutrophil, apoptotic cell, and epithelium. All annotations were reviewed and corrected, where needed, by a dermatopathologist (W.B.).</li>\n<li><strong>Tissue segmentation<br></strong>Tissue segmentations were created manually using QuPath by M.S., with the following categories: tumor, stroma, epidermis, necrosis, blood vessel, and background. Annotations were reviewed and corrected, where needed, by a dermatopathologist (W.B.).</li>\n</ul>\n<p><strong>Quality Control</strong>: <br>To assess the reliability of the annotations, intra- and interobserver agreement (by pathologist G.B.) were determined on 12 randomly selected ROIs.</p>\n<ul>\n<li><strong>Nuclei segmentation<br></strong>The intraobserver overall precision was 84.89%, with a recall of 86.45%, and an F1 score of 85.66%. Interobserver overall precision was 80.34%, with a recall of 80.62%, and an F1 score of 80.20%. These results are based on the sum of all true positive, false positive, and false negative counts for the 12 ROIs.</li>\n<li><strong>Tissue segmentation<br></strong>The DICE score was determined on the same 12 randomly selected ROIs. The average intraobserver DICE score was 0.90, and the interobserver DICE score was also 0.90.</li>\n</ul>\n<p> </p>\n<p><strong>Version 3</strong>:<br>Removed sample \"training_set_metastatic_roi_103\" due to inconsistencies in annotation file.</p>\n<p><strong>Version 4:<br></strong>Sample training_set_metastatic_roi_088 missed one color annotation for a nuclei_apoptosis in the geojson file rendering it qupath uncompatible. This is fixed in the new version. </p>\n<p><strong>Version 5:<br></strong>Addition of correct sample of training_set_metastatic_roi_103\" after deadline of <a href=\"https://puma.grand-challenge.org/\">panoptic segmentation of nuclei and tissue in advanced melanoma challenge</a> test phase. </p>",
"access_right": "open",
"creators": [
{
"name": "Schuiveling, Mark",
"affiliation": "University Medical Center Utrecht",
"orcid": "0000-0002-2631-7271"
}
],
"contributors": [
{
"name": "Blokx, Willeke",
"affiliation": "University Medical Center Utrecht",
"type": "DataCurator",
"orcid": "0000-0002-4647-8830"
},
{
"name": "Breimer, Gerben",
"affiliation": "University Medical Center Utrecht",
"type": "DataCurator",
"orcid": "0000-0003-0365-3667"
}
],
"keywords": [
"TILs",
"Histopathology",
"Nuclei Segmentation"
],
"related_identifiers": [
{
"identifier": "10.1093/gigascience/giaf011",
"relation": "isDescribedBy",
"resource_type": "publication-article",
"scheme": "doi"
}
],
"resource_type": {
"title": "Image",
"type": "image"
},
"journal": {
"pages": "giaf011",
"title": "GigaScience",
"volume": "14"
},
"meeting": {
"title": "Medical Imaging with Deep Learning",
"acronym": "MIDL",
"dates": "03-05 July 2024",
"place": "Paris",
"url": "https://openreview.net/forum?id=CWC4amo4qn"
},
"license": {
"id": "cc-zero"
},
"relations": {
"version": [
{
"index": 4,
"is_last": true,
"parent": {
"pid_type": "recid",
"pid_value": "10940193"
}
}
]
}
},
"title": "Melanoma Histopathology Dataset with Tissue and Nuclei Annotations",
"links": {
"self": "https://zenodo.org/api/records/15050523",
"self_html": "https://zenodo.org/records/15050523",
"preview_html": "https://zenodo.org/records/15050523?preview=1",
"doi": "https://doi.org/10.5281/zenodo.15050523",
"self_doi": "https://doi.org/10.5281/zenodo.15050523",
"self_doi_html": "https://zenodo.org/doi/10.5281/zenodo.15050523",
"reserve_doi": "https://zenodo.org/api/records/15050523/draft/pids/doi",
"parent": "https://zenodo.org/api/records/10940193",
"parent_html": "https://zenodo.org/records/10940193",
"parent_doi": "https://doi.org/10.5281/zenodo.10940193",
"parent_doi_html": "https://zenodo.org/doi/10.5281/zenodo.10940193",
"self_iiif_manifest": "https://zenodo.org/api/iiif/record:15050523/manifest",
"self_iiif_sequence": "https://zenodo.org/api/iiif/record:15050523/sequence/default",
"files": "https://zenodo.org/api/records/15050523/files",
"media_files": "https://zenodo.org/api/records/15050523/media-files",
"latest": "https://zenodo.org/api/records/15050523/versions/latest",
"latest_html": "https://zenodo.org/records/15050523/latest",
"versions": "https://zenodo.org/api/records/15050523/versions",
"draft": "https://zenodo.org/api/records/15050523/draft",
"access_links": "https://zenodo.org/api/records/15050523/access/links",
"access_grants": "https://zenodo.org/api/records/15050523/access/grants",
"access_users": "https://zenodo.org/api/records/15050523/access/users",
"access_request": "https://zenodo.org/api/records/15050523/access/request",
"access": "https://zenodo.org/api/records/15050523/access",
"communities": "https://zenodo.org/api/records/15050523/communities",
"communities-suggestions": "https://zenodo.org/api/records/15050523/communities-suggestions",
"request_deletion": "https://zenodo.org/api/records/15050523/request-deletion",
"file_modification": "https://zenodo.org/api/records/15050523/file-modification",
"quota_increase": "https://zenodo.org/api/records/15050523/quota-increase",
"requests": "https://zenodo.org/api/records/15050523/requests"
},
"updated": "2025-03-19T15:54:57.783820+00:00",
"recid": "15050523",
"revision": 4,
"files": [
{
"id": "c94325cc-0854-4648-96b5-e5bad959dd60",
"key": "01_training_dataset_tif_ROIs.zip",
"size": 646953540,
"checksum": "md5:1e16d440f0f94156fd5c0ea3f082bd90",
"links": {
"self": "https://zenodo.org/api/records/15050523/files/01_training_dataset_tif_ROIs.zip/content"
}
},
{
"id": "478fdd67-c1d0-49ef-9259-71bb1e85ffad",
"key": "01_training_dataset_tif_context_ROIs.zip",
"size": 14207180919,
"checksum": "md5:bb7e6dbea1bde6b4e5221d0eebc2b378",
"links": {
"self": "https://zenodo.org/api/records/15050523/files/01_training_dataset_tif_context_ROIs.zip/content"
}
},
{
"id": "c129e7c4-197b-421c-9ffa-28e0dba4815a",
"key": "01_training_dataset_geojson_nuclei.zip",
"size": 15975301,
"checksum": "md5:1f695c66db9461251c581e850f73c044",
"links": {
"self": "https://zenodo.org/api/records/15050523/files/01_training_dataset_geojson_nuclei.zip/content"
}
},
{
"id": "301e4f96-e0c4-4ee0-b3e6-cfd52e5d072b",
"key": "01_training_dataset_geojson_tissue.zip",
"size": 3070756,
"checksum": "md5:d9a1d94f078068ed26687cca28b24baf",
"links": {
"self": "https://zenodo.org/api/records/15050523/files/01_training_dataset_geojson_tissue.zip/content"
}
}
],
"swh": {},
"owners": [
{
"id": "1093778"
}
],
"status": "published",
"stats": {
"downloads": 41084,
"unique_downloads": 14217,
"views": 9607,
"unique_views": 8554,
"version_downloads": 28117,
"version_unique_downloads": 6426,
"version_unique_views": 2124,
"version_views": 2337
},
"state": "done",
"submitted": true
}
|