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.
Sample Collection:
\nRegions 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.
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.
Annotation Process:
\nQuality Control:
To assess the reliability of the annotations, intra- and interobserver agreement (by pathologist G.B.) were determined on 12 randomly selected ROIs.
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Version 3:
Removed sample \"training_set_metastatic_roi_103\" due to inconsistencies in annotation file.
Version 4:
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.
Version 5:
Addition of correct sample of training_set_metastatic_roi_103\" after deadline of panoptic segmentation of nuclei and tissue in advanced melanoma challenge test phase.