Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke.

ABSTRACT: Thrombus volume in posterior circulation stroke (PCS) has been associated with outcome, through recanalization. Manual thrombus segmentation is impractical for large scale analysis of image characteristics. Hence, in this study we develop the first automatic method for thrombus localization and segmentation on CT in patients with PCS. In this multi-center retrospective study, 187 patients with PCS from the MR CLEAN Registry were included. We developed a convolutional neural network (CNN) that segments thrombi and restricts the volume-of-interest (VOI) to the brainstem (Polar-UNet). Furthermore, we reduced false positive localization by removing small-volume objects, referred to as volume-based removal (VBR). Polar-UNet is benchmarked against a CNN that does not restrict the VOI (BL-UNet).

SUBMITTER: Zoetmulder R 

PROVIDER: S-EPMC9222185 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature