BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision
Prantik Deb Lalith Bharadwaj Baru Kamalaker Dadi Bapi Raju S
Brain stroke has become a significant burden on global health and thus we
need remedies and prevention strategies to overcome this challenge. For this,
the immediate identification of stroke and risk stratification is the primary
task for clinicians. To aid expert clinicians, automated segmentation models
are crucial. In this work, we consider the publicly available dataset ATLAS
$v2.0$ to benchmark various end-to-end supervised U-Net style models.
Specifically, we have benchmarked models on both 2D and 3D brain images and
evaluated them using standard metrics. We have achieved the highest Dice score
of 0.583 on the 2D transformer-based model and 0.504 on the 3D residual U-Net
respectively. We have conducted the Wilcoxon test for 3D models to correlate
the relationship between predicted and actual stroke volume. For
reproducibility, the code and model weights are made publicly available:
https://github.com/prantik-pdeb/BeSt-LeS.