README.md

metadata

license: cc-by-nc-4.0
tags:
  - computational-pathology
  - colorectal-liver-metastases
  - tissue-segmentation
  - whole-slide-imaging
library_name: keras
extra_gated_prompt: >-
  These weights are derived from clinical research data and are released for
  non-commercial research use only. They are not a medical device and must not
  be used for clinical decision making. By requesting access you agree to these
  terms.
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text
  I agree to use these weights for non-commercial research only: checkbox

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:

1. bg_network.net (28 MB): background versus tissue segmentation, run first at 2.0 micron spacing to create the tissue mask.
2. 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.