Intracranial Vessel Wall Segmentation with Deep Learning Using a Novel Tiered Loss Function to Incorporate Class Inclusion

Abstract

The goal of this study is to develop an automated vessel wall segmentation method on T1-weighted intracranial vessel wall magnetic resonance images, with a focus on modeling the inclusion relation between the inner and outer boundaries of the vessel wall. We propose a novel method that estimates the inner and outer vessel wall boundaries simultaneously, using a network with a single output channel resembling the level-set function height. The network is driven by a unique tiered loss that accounts for data fidelity of the lumen and vessel wall classes and a length regularization to encourage boundary smoothness. The proposed method achieved Dice similarity coefficients (DSC) in 2D of 0.925 ± 0.048, 0.786 ± 0.084, Hausdorff distance (HD) of 0.286 ± 0.436 mm, 0.345 ± 0.419 mm, and mean surface distance (MSD) of 0.083 ± 0.037 mm and 0.103 ± 0.032 mm for the lumen and vessel wall, respectively, on a test set; compared favorably to a benchmark UNet model that achieved DSC 0.924 ± 0.047, 0.794 ± 0.082, HD 0.298 ± 0.477 mm, 0.394 ± 0.431 mm, and MSD 0.087 ± 0.056 mm, 0.119 ± 0.059 mm.