nva_atlas


Logo for nva_atlas

About

Creators:

User Mugshot amrn 

User Mugshot ahutton 

User Mugshot yangd05 

Contact email:
Image Version:
de07c1a4-bf3a-4f43-9099-f5cbd9706c74
Last updated:
Aug. 18, 2022, 9:30 a.m.

Interfaces

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

Inputs Outputs
1
  • T1 Brain MRI (Image)
  • Stroke Lesion Segmentation (Segmentation)
  • 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