Automatic Carotid Plaque Segmentation Using Deep Learning Model with Multi-Head Loss Integrating Anatomical Features

Abstract

Motivation: With the assistance of prior vessel wall mask, segmentation of atherosclerotic plaque can achieve satisfactory performance. However, manual sketching of vessel wall mask is still time-consuming. Goal(s): To propose a method for fast and accurate plaque segmentation without relying on prior knowledge of vessel walls. Approach: This study proposes a deep learning model based on a multi-head loss design for automatic segmentation of carotid artery plaques, with the aim of reducing dependence on prior information of vessel walls in plaque segmentation. Results: In the independent test, the model with the multi-head loss design achieving excellent results similar to using vessel wall prior. Impact: This study achieved fully automatic and accurate plaque segmentation without manual priors, which will greatly reduce burden of radiologist to segment and quantify plaque, and also contribute to more efficient stroke risk assessment, progress monitoring, and efficacy evaluation for patient.