Intracranial Vessel Wall Segmentation With Deep Learning Using A Novel Tiered Loss Function To Incorporate Class Inclusion

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 В± 0.048, 0.786 В± 0.084, Hausdorff distance (HD) of 0.286 В± 0.436 mm, 0.345 В± 0.419 mm, and mean surface distance (MSD) of 0.083 В± 0.037 mm and 0.103 В± 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 В± 0.047, 0.794 В± 0.082, HD 0.298 В± 0.477 mm, 0.394 В± 0.431 mm, and MSD 0.087 В± 0.056 mm, 0.119 В± 0.059 mm.