Key result
The fully automatic Auto-VWV framework using CAP-UNet achieved better vessel wall volume measurement reproducibility than intra- and inter-operator manual measurements on 3D ultrasound images.
Why the study?
Carotid artery vessel-wall-volume measurement on 3D ultrasound aids atherosclerosis assessment, but manual measurement is subjective and operator-dependent, with fully automatic tools lacking.
Does a fully automatic vessel wall volume measurement framework using CAP-UNet improve segmentation accuracy and measurement reproducibility compared to manual measurements and competing models in 3D carotid artery ultrasound images?
Does a fully automatic vessel wall volume measurement framework using CAP-UNet improve segmentation accuracy and measurement reproducibility compared to manual measurements and competing models in 3D carotid artery ultrasound images?
A novel fully automatic deep learning framework (CAP-UNet) improves the accuracy and reproducibility of vessel wall volume measurements in 3D carotid ultrasound images compared to manual methods.
The vessel-wall-volume (VWV) measured based on three-dimensional (3D) carotid artery (CA) ultrasound (US) images can help to assess carotid atherosclerosis and manage patients at risk for stroke. Manual involvement for measurement work is subjective and requires well-trained operators, and fully automatic measurement tools are not yet available. Thereby, we proposed a fully automatic VWV measurement framework (Auto-VWV) using a CA prior-knowledge embedded U-Net (CAP-UNet) to measure the VWV from 3D CA US images without manual intervention. The Auto-VWV framework is designed to improve the repeated VWV measuring consistency, which resulted in the first fully automatic framework for VWV measurement. CAP-UNet is developed to improve segmentation accuracy on the whole CA, which composed of a U-Net type backbone and three additional prior-knowledge learning modules. Specifically, a continuity learning module is used to learn the spatial continuity of the arteries in a sequence of image slices. A voxel evolution learning module was designed to learn the evolution of the artery in adjacent slices, and a topology learning module was used to learn the unique topology of the carotid artery. In two 3D CA US datasets, CAP-UNet architecture achieved state-of-the-art performance compared to eight competing models. Furthermore, CAP-UNet-based Auto-VWV achieved better accuracy and consistency than Auto-VWV based on competing models in the simulated repeated measurement. Finally, using 10 pairs of real repeatedly scanned samples, Auto-VWV achieved better VWV measurement reproducibility than intra- and inter-operator manual measurements. The code is available at https://github.com/Yue9603/Auto-VWV.
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Jiang et al. (2024) studied Carotid atherosclerosis (n=20). Auto-VWV framework using CAP-UNet vs. Competing models and manual measurements was evaluated on Vessel wall volume measurement reproducibility and segmentation accuracy. The fully automatic Auto-VWV framework using CAP-UNet achieved better vessel wall volume measurement reproducibility than intra- and inter-operator manual measurements on 3D ultrasound images.
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