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About

Interfaces

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

Model Facts

Summary

We implemented our approach with MONAI. We use the encoder-decoder backbone to extract image features and a smaller decoder to reconstruct the segmentation mask.

Mechanism

We have used the ATLAS dataset for training the model. We have randomly split the entire dataset into 5-folds and trained a model for each fold.

Validation and Performance

Average DICE among classes using 5-fold cross-validation: 0.6463 0.6621 , 0.6578, 0.6576, 0.6353 , 0.6188

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