Accurate stroke lesion segmentation plays a pivotal role in stroke rehabilitation research, to provide lesion shape and size information which can be used for quantification of the extent of the stroke and to assess treatment efficacy. Recently, automatic segmentation algorithms using deep learning techniques have been developed and achieved promising results. In this report, we present our stroke lesion segmentation model based on nnU-Net framework, and apply it to the Anatomical Tracings of Lesions After Stroke (ATLAS v2.0) dataset. Furthermore, we describe an effective post-processing strategy that can improve some segmentation metrics. Our method took the first place in the 2022 MICCAI ATLAS Challenge with an average Dice score of 0.6667, Lesion-wise F1 score of 0.5643, Simple Lesion Count score of 4.5367, and Volume Difference score of 8804.9102. Our code and trained model weights are publicly available at https://github.com/King-HAW/ATLAS-R2-Docker-Submission.
@article{arxiv.2211.15486,
title = {MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation},
author = {Jiayu Huo and Liyun Chen and Yang Liu and Maxence Boels and Alejandro Granados and Sebastien Ourselin and Rachel Sparks},
journal= {arXiv preprint arXiv:2211.15486},
year = {2022}
}
Comments
Challenge Report, 1st place in 2022 MICCAI ATLAS Challenge