DOI: 10.5281/zenodo.7152243
title: Digital Pathology Dataset for Prostate Cancer Diagnosis

description: Links to code and <em>Patterns</em> paper: 1. Multi-lens Neural Machine (MLNM) Code 2. An AI-assisted Tool For Efficient Prostate Cancer Diagnosis in Low-grade and Low-volume Cases Digitized hematoxylin and eosin (H&E)-stained whole-slide-images (WSIs) of 40 prostatectomy and 59 core needle biopsy specimens were collected from 99 prostate cancer patients at Tan Tock Seng Hospital, Singapore. There were 99 WSIs in total such that each specimen had one WSI. H&E-stained slides were scanned at 40× magnification (specimen-level pixel size 0·25μm × 0·25μm) using Aperio AT2 Slide Scanner (Leica Biosystems). Institutional board review from the hospital were obtained for this study, and all the data were de-identified. Prostate glandular structures in core needle biopsy slides were manually annotated and classified using the ASAP annotation tool (ASAP). A senior pathologist reviewed 10% of the annotations in each slide, ensuring that some reference annotations were provided to the researcher at different regions of the core. It is to be noted that partial glands appearing at the edges of the biopsy cores were not annotated. <strong>Whole Slide Image Dataset </strong> Whole Slide Image dataset containing 99 images in SVS format with corresponding annotations in XML format are provided in WSI.zip. Available patient grading for the WSIs are provided in 'gleason_score_mapped.txt'. These XML annotations can be parsed using the code in official repository. <strong>Cropped Image Dataset </strong> Patches of size 512 × 512 pixels were cropped from the WSI (Whole Slide Image Dataset) at resolutions 5×, 10×, 20×, and 40× with an annotated gland centered at each patch. This dataset contains these cropped images. This dataset is used to train the two AI models for Gland Segmentation (99 patients) and Gland Classification (46 patients). Tables 1 and 2 illustrate both gland segmentation and gland classification datasets. We have put the two corresponding sub-datasets as two zip files as follows: gland_segmentation_dataset.zip gland_classification_dataset.zip <strong>Table 1:</strong> The number of slides and patches in training, validation, and test sets for gland segmentation task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen. <strong>#Slides</strong> Train Valid Test <strong>Total</strong> Prostatectomy 17 8 15 40 Biopsy 26 13 20 59 <strong>Total</strong> 43 21 35 99 <strong>#Patches</strong> Train Valid Test <strong>Total</strong> Prostatectomy 7795 3753 7224 18772 Biopsy 5559 4028 5981 15568 <strong>Total</strong> 13354 7781 13205 34340 <strong>Table 2:</strong> The number of slides and patches in training, validation, and test sets for gland classification task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen. The gland classification datasets are the subsets of the gland segmentation datasets. <strong>GS</strong>: Gleason Score. <strong>B</strong>: Benign. <strong>M</strong>: Malignant. <strong>#Slides (GS 3+3:3+4:4+3)</strong> Train Valid Test <strong>Total</strong> Biopsy 10:9:1 3:7:0 6:10:0 19:26:1 <strong>#Patches (B:M)</strong> Train Valid Test <strong>Total</strong> Biopsy 1557:2277 1216:1341 1543:2718 4316:6336 <strong>NB:</strong> Gland classification folder (gland_classification_dataset.zip) may contain extra patches, labels of which could not be identified from H&E slides. They were not used in the machine learning study.
description: This study was funded by the Biomedical Research Council of the Agency for Science, Technology and Research, Singapore.