Segmentation of Carotid Arteries From Three-Dimensional Black-Blood Magnetic Resonance Imaging With Sparse Annotation Using a Multi-Dimensional Hybrid Model

Quantification of carotid atherosclerosis is important in monitoring patients at risk of cardiovascular events and in evaluating therapies. High-resolution 3D carotid magnetic resonance imaging (MRI) has been developed to provide extended coverage of the carotid arteries. However, the extended coverage poses a challenge as several hundreds of 2D axial images are required to be segmented for analysis. We propose a multi-dimensional hybrid framework that requires only a sparse set of manual segmentation. Dense surrogate ground truth boundaries required to train the framework are automatically generated by propagating the sparse manual segmentation using the proposed region of interest (ROI) U-Net. Furthermore, the Point U-Net was developed to generate surrogate ground truth for carotid branches without manual segmentation. The proposed framework leverages the advantages of 3D and 2D convolution neural networks (CNNs) to segment the outer wall and lumen from 3D MRI. The 3D multiscale U-Net provides a rough outer wall segmentation, which serves as the ROI to guide outer wall and lumen segmentation by the 2D ROI U-Net. The 3D Multiscale U-Net localizes the ROI automatically, bypassing the need for manual ROI identification. The 3D Multiscale U-Net was further improved by a 3D inception module installed at the bottleneck and the novel loss functions that promote longitudinal continuity and minimize the overlap of the internal and external carotid arteries. Extensive evaluation on the publicly available Carotid Artery Vessel Wall Segmentation challenge dataset shows that our approach outperforms the top-ranked solution in the challenge and state-of-the-art segmentation methods.