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CRLM liver tissue segmentation
Whole slide image (WSI) tissue segmentation for hematoxylin and eosin sections
of colorectal liver metastases (CRLM). This model produces the tissue mask used
upstream of the desmoplastic status and survival pipeline
(computationalpathologygroup/pathology-liver-survival).
Method
The model is a U-Net (Keras backend), depth 5, branching factor 5, with batch
normalization and valid padding. It is trained with categorical cross-entropy on
412 by 412 patches at 0.5 micron pixel spacing, with RGB range normalization to
0 to 1. Inference is run through the applynetwork_multiproc.py script from the
pathology-fast-inference code base.
Two networks run in sequence:
bg_network.net(28 MB): background versus tissue segmentation, run first at 2.0 micron spacing to create the tissue mask.nn1_best_model.net(87 MB): the main multi-class tissue segmentation, run at 0.5 micron spacing using the tissue mask from step 1.
The network configuration in the code repository maps mask labels to 10 output classes. The exact tissue class definitions should be confirmed with the authors and against the Grand Challenge algorithm before interpretation.
Contents
nn1_best_model.net: main multi-class tissue segmentation U-Net.bg_network.net: background versus tissue network used to create the tissue mask.
Intended use
Research use only. Not a medical device. Not for clinical decision making.
Links
- Code: https://github.com/DIAGNijmegen/pathology-liver-tissue-segmentation
- Grand Challenge: https://grand-challenge.org/algorithms/colorectal-liver-metastases-segmentation-in-he/
- Downstream survival model: https://huggingface.co/computationalpathologygroup/pathology-liver-survival
Citation
Confirm the correct paper and citation with the authors before use.
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