  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.
