  In this paper, we demonstrate the feasibility and performance of deep
residual neural networks for volumetric segmentation of irreversibly damaged
brain tissue lesions on T1-weighted MRI scans for chronic stroke patients. A
total of 239 T1-weighted MRI scans of chronic ischemic stroke patients from a
public dataset were retrospectively analyzed by 3D deep convolutional
segmentation 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 using
the manual tracing of lesions as the reference standard. Bootstrapping was
employed for all metrics to estimate 95% confidence intervals. The models were
assessed on the test set of 31 scans. The average DSC was 0.64 (0.51-0.76) with
a median of 0.78. ASSD and HD were 3.6 mm (1.7-6.2 mm) and 20.4 mm (10.0-33.3
mm), respectively. To the best of our knowledge, this performance is the
highest achieved on this public dataset. The latest deep learning architecture
and techniques were applied for 3D segmentation on MRI scans and demonstrated
to be effective for volumetric segmentation of chronic ischemic stroke lesions.
