English

Automated ischemic stroke lesion segmentation from 3D MRI

Image and Video Processing 2022-09-22 v2 Computer Vision and Pattern Recognition

Abstract

Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a platform for researchers to compare their solutions to 3D segmentation of ischemic stroke regions from 3D MRIs. In this work, we describe our solution to ISLES 2022 segmentation task. We re-sample all images to a common resolution, use two input MRI modalities (DWI and ADC) and train SegResNet semantic segmentation network from MONAI. The final submission is an ensemble of 15 models (from 3 runs of 5-fold cross validation). Our solution (team name NVAUTO) achieves the top place in terms of Dice metric (0.824), and overall rank 2 (based on the combined metric ranking).

Keywords

Cite

@article{arxiv.2209.09546,
  title  = {Automated ischemic stroke lesion segmentation from 3D MRI},
  author = {Md Mahfuzur Rahman Siddique and Dong Yang and Yufan He and Daguang Xu and Andriy Myronenko},
  journal= {arXiv preprint arXiv:2209.09546},
  year   = {2022}
}

Comments

ISLES22 challenge report, MICCAI2022

R2 v1 2026-06-28T01:43:12.260Z