Lung Cancer Segmentation


Logo for Lung Cancer Segmentation

About

Contact email:
Image Version:
50577ada-46fe-431d-8064-ad11e68c3c2d
Last updated:
May 3, 2022, 6:57 a.m.

Interfaces

This algorithm implements all of the following input-output combinations:

Inputs Outputs
1
  • Generic Medical Image (Image)
  • Generic Overlay (Heat Map)
  • Results JSON File (Anything)
  • Model Facts

    Summary

    The model was trained on ACDC-HP challenge and got 3rd place in post-challenge leaderboard. It was further fine-tuned with data from the TCGA-LUAD and TCGA-LUSC projects.

    The input images should be whole-slide images, with formats including 'svs', 'tif' etc. The images should have three channels (RGB) and a pixel spacing of about 0.5μm/pixel.

    Mechanism

    The model is based on U-Net, and trained with data from ACDC-HP challenge, as well as in-house annotations of TCGA-LUAD and TCGA-LUSC

    Validation and Performance

    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