Tissue-Background Segmentation
About
Interfaces
This algorithm implements all of the following input-output combinations:
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Model Facts
Summary
Tissue-background segmentation of histopathological whole-slide images. In this project we collected 100 + 8 whole-slide images (WSIs) for development ant testing of a fully-convolutional neural network (FCNN) that can distinguish between tissue and backgound areas on the WSIs.
Mechanism
This model is a fully-convolutional neural network consisting of 7 convolutional layers with ReLU (Maas et al., 2013) activation function in the first 6 convolutional layers and softmax in the last one. Max pooling was inserted after each of the first 3 convolutional layers to reduce the memory requirements of the network.
The model was trained with WSI in tiff format of breast, lymph node, kidney, tongue, rectum, and lung tissue. The slides were stained with 6 different stains: hematoxylin and eosin (H&E), Sirius Red, Periodic Acid-Schiff (PAS), cytokeratin AE1/AE3 (AE1AE3), Ki-67, and a cocktail of cytokeratin 8 and cytokeratin 18 (CK8-18).
Validation and Performance
The model was validated with a dataset containing lung, cornea, aorta, brain, skin, uterus, and kidney tissue samples. The tissues were stained with Grocott, Alcian Blue, Von Kossa, Perls, and Chromotrope Aniline Blue (CAB) stains. Only lung and kidney tissues were present in both of the datasets, while the stains are non-overlapping.
Uses and Directions
This algorithm was developed for research purposes only.
Warnings
Common Error Messages
Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3