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In this paper, we demonstrate the feasibility and performance of deepresidual neural networks for volumetric segmentation of irreversibly damagedbrain tissue lesions on T1-weighted MRI scans for chronic stroke patients. Atotal of 239 T1-weighted MRI scans of chronic ischemic stroke patients from apublic dataset were retrospectively analyzed by 3D deep convolutionalsegmentation models with residual learning, using a novel zoom-in&out strategy.Dice similarity coefficient (DSC), Average symmetric surface distance (ASSD),and Hausdorff distance (HD) of the identified lesions were measured by usingthe manual tracing of lesions as the reference standard. Bootstrapping wasemployed for all metrics to estimate 95% confidence intervals. The models wereassessed on the test set of 31 scans. The average DSC was 0.64 (0.51-0.76) witha median of 0.78. ASSD and HD were 3.6 mm (1.7-6.2 mm) and 20.4 mm (10.0-33.3mm), respectively. To the best of our knowledge, this performance is thehighest achieved on this public dataset. The latest deep learning architectureand techniques were applied for 3D segmentation on MRI scans and demonstratedto be effective for volumetric segmentation of chronic ischemic stroke lesions.

Automatic Post-Stroke Lesion Segmentation on MR Images using 3D Residual Convolutional Neural Network