Volumetric adversarial training for ischemic stroke lesion segmentation

Abstract

Ischemic stroke is one of the most common and yet deadly cerebrovascular diseases. Identifying lesion area is an essential step for stroke management and outcome assessment. Currently, manual delineation is the gold standard for clinical diagnosis. However, inter-annotator variances and labor-intensive nature of manual labeling can lead to observer bias or potential disagreement of between annotators. While incorporating a computer-aided diagnosis system may alleviate these issues, other challenges such as highly varying shapes and difficult boundaries in the lesion area make the designing of such system non-trivial. To address these issues, we propose a novel adversarial training paradigm for segmenting ischemic stroke lesion. The training procedure involves the main segmentation network and an auxiliary critique network. The segmentation network is a 3D residual U-net that produces a segmentation mask in each training iteration while critique network enforces high-level constraints on the segmentation network to produce predictions that mimic the ground truth distribution. We applied the proposed model on the 2018 ISLES stroke lesion segmentation challenge dataset and achieved competitive results on the training dataset.