Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
The analysis of carotid arteries, particularly plaques, in multi-sequence
Magnetic Resonance Imaging (MRI) data is crucial for assessing the risk of
atherosclerosis and ischemic stroke. In order to evaluate metrics and radiomic
features, quantifying the state of atherosclerosis, accurate segmentation is
important. However, the complex morphology of plaques and the scarcity of
labeled data poses significant challenges. In this work, we address these
problems and propose a semi-supervised deep learning-based approach designed to
effectively integrate multi-sequence MRI data for the segmentation of carotid
artery vessel wall and plaque. The proposed algorithm consists of two networks:
a coarse localization model identifies the region of interest guided by some
prior knowledge on the position and number of carotid arteries, followed by a
fine segmentation model for precise delineation of vessel walls and plaques. To
effectively integrate complementary information across different MRI sequences,
we investigate different fusion strategies and introduce a multi-level
multi-sequence version of U-Net architecture. To address the challenges of
limited labeled data and the complexity of carotid artery MRI, we propose a
semi-supervised approach that enforces consistency under various input
transformations. Our approach is evaluated on 52 patients with
arteriosclerosis, each with five MRI sequences. Comprehensive experiments
demonstrate the effectiveness of our approach and emphasize the role of fusion
point selection in U-Net-based architectures. To validate the accuracy of our
results, we also include an expert-based assessment of model performance. Our
findings highlight the potential of fusion strategies and semi-supervised
learning for improving carotid artery segmentation in data-limited MRI
applications.
