Intracranial Vessel Wall Segmentation with Deep Learning using a Novel Tiered Loss Function Incorporating Class Inclusion.

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Abstract

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.Implemented with a 2.5D UNet with a ResNet backbone, the proposed method achieved Dice similarity coefficients (DSC) in 2D of 0.925 ± 0.048, 0.786 ± 0.084, Hausdorff distance of 0.286 ± 0.436mm, 0.345 ± 0.419mm and mean surface distance (MSD) of 0.083 ± 0.037mm and 0.103 ± 0.032mm for the lumen and vessel wall, respectively, on a test set; compared favorably to a baseline UNet model that achieved DSC 0.924 ± 0.047, 0.794 ± 0.082, HD 0.298 ± 0.477mm, 0.394 ± 0.431mm, and MSD 0.087 ± 0.056mm, 0.119 ± 0.059mm. Our vessel wall segmentation method achieved substantial improvement in morphological integrity and accuracy compared to benchmark methods.The proposed method provides a systematic approach to model the inclusion morphology and incorporate it into an optimization infrastructure. It can be applied to any application where inclusion exists among a (sub)set of classes to be segmented. Improved feasibility in result morphology promises better support for clinical quantification and decision. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.