A dataset and a methodology for intraoperative computer-aided diagnosis of a metastatic colon cancer in a liver. (April 2021)

• Record Type: Journal Article
• Authors: Sitnik, Dario; Aralica, Gorana; Hadžija, Mirko; Hadžija, Marijana Popović; Pačić, Arijana; Periša, Marija Milković; Manojlović, Luka; Krstanac, Karolina; Plavetić, Andrija; Kopriva, Ivica
• Is Part Of: Biomedical signal processing and control. Volume 66(2021)
• Publication Date: 2021-04
• Subjects: Intraoperative diagnosis -- Metastatic colon cancer -- Liver -- Stain normalization -- U-Net(++) -- DeepLabv3; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102402
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: Publicly available dataset with 82 H&E stained images of frozen sections. Images are acquired on 19 patients with metastatic colon cancer in a liver. Pixel wise ground truths provided by seven domain experts. Diagnostic results obtained with SVM, kNN, U-Net, U-Net++ and deeplabv3 classifiers. Balanced accuracy and F1 score on independent test set amount to 89.34% and 83.67%. Abstract: The lack of pixel-wise annotated images severely hinders the deep learning approach to computer-aided diagnosis in histopathology. This research creates a public database comprised of: ( i ) a dataset of 82 histopathological images of hematoxylin-eosin stained frozen sections acquired intraoperatively on 19 patients diagnosed with metastatic colon cancer in a liver; ( ii ) corresponding pixel-wise ground truth maps annotated by four pathologists, two residents in pathology, and one final-year student of medicine. The Fleiss' kappa equal to 0.74 indicates substantial inter-annotator agreement; ( iii ) two datasets with images stain-normalized relative to two target images; ( iv ) development of two conventional machine learning and three deep learning-based diagnostic models. The database is available at http://cocahis.irb.hr . For binary, cancer vs. non-cancer, pixel-wise diagnosis we develop: SVM, kNN, U-Net, U-Net++, and DeepLabv3 classifiers that combine results from original images and stain-normalized images, which can be interpreted as different views. On average, deep learning classifiers outperformed SVM and kNN classifiers on an independent test set 14% in terms of micro balanced accuracy, 15% in terms of the micro F1 score, and 26% in terms of micro precision. As opposed to that, the difference in performance between deep classifiers is within 2%. We found an insignificant difference in performance between deep classifiers trained from scratch and corresponding classifiers pre-trained on non-domain image datasets. The best micro balanced accuracy estimated on the independent test set by the U-Net++ classifier equals 89.34%. Corresponding amounts of F1 score and precision are, respectively, 83.67% and 81.11%.