VWV-SSL: Carotid vessel-wall-volume segmentation via sequence structural similarity and augmentation...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Research

Background:

• Vessel wall volume (VWV) quantifies carotid atherosclerosis progression using 3D ultrasound.
• Accurate VWV measurement necessitates segmenting media-adventitia (MAB) and lumen intima boundaries (LIB).
• Deep learning requires extensive annotated data, posing a challenge for 3D ultrasound segmentation.

Purpose of the Study:

• To develop a novel self-supervised learning (SSL) algorithm, VWV-SSL, for 3D carotid ultrasound (3DUS) image segmentation.
• To improve VWV measurement accuracy by leveraging sequence structural similarity and feature consistency in 3DUS images.
• To reduce the reliance on large annotated datasets for training deep learning models in carotid atherosclerosis assessment.

Main Methods:

• Proposed VWV-SSL algorithm utilizing sequence structural similarity and strong-weak augmented feature consistency for self-supervised training.
• Applied VWV-SSL to the 3D U-Net architecture for segmenting MAB and LIB in 3DUS images.
• Evaluated performance on 1158 3D US datasets from 250 subjects, comparing with baseline and state-of-the-art SSL methods using limited labeled data.

Main Results:

• VWV-SSL demonstrated significant improvements in segmentation performance compared to baseline networks when trained on small labeled datasets (15, 45, 75 subjects).
• The proposed method outperformed existing state-of-the-art SSL algorithms in segmentation accuracy.
• VWV-SSL effectively enhanced the feature learning capabilities of the 3D U-Net for vessel segmentation.

Conclusions:

• VWV-SSL offers a promising solution for accurate 3D carotid ultrasound segmentation with reduced annotation requirements.
• The method facilitates improved performance of 3D U-Net models trained on limited labeled data for VWV measurement.
• VWV-SSL has potential for clinical application in monitoring carotid atherosclerosis progression and regression.