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| "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>", |
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| "name": "Schuiveling, Mark", |
| "affiliation": "University Medical Center Utrecht", |
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| { |
| "name": "Blokx, Willeke", |
| "affiliation": "University Medical Center Utrecht", |
| "type": "DataCurator", |
| "orcid": "0000-0002-4647-8830" |
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| { |
| "name": "Breimer, Gerben", |
| "affiliation": "University Medical Center Utrecht", |
| "type": "DataCurator", |
| "orcid": "0000-0003-0365-3667" |
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| "TILs", |
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| "Nuclei Segmentation" |
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| "title": "Medical Imaging with Deep Learning", |
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