Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network

Abstract

Cerebral infarct volume measured in follow-up non-contrast CT (NCCT) scans is an important radiologic outcome measure evaluating the effectiveness of endovascular therapy of acute ischemic stroke (AIS) patients. In this paper, a dense Multi-Path Contextual Generative Adversarial Network (MPC-GAN) is proposed to automatically segment ischemic infarct volume from NCCT images of AIS patients. The developed MPC-GAN approach makes use of a dense multi-path U-Net as generator regularized by a discriminator network. Both generator and discriminator take contextual information as inputs, such as bilateral intensity difference, infarct location probability, and distance to cerebrospinal fluid (CSF). We collected 100 NCCT images with manual segmentations. Of 100 patients, 60 patients were used to train the MPC-GAN, 10 patients were used to tune the parameters, and the remained 30 patients were used for validation. Quantitative results in comparison with manual segmentations show that the proposed MPC-GAN achieved a dice coefficient (DC) of 72.6%, outperforming some state-of-the-art segmentation methods, such as U-Net, U-Net based GAN, and random forest based segmentation method.