The application of the nnU-Net-based automatic segmentation model in assisting carotid artery stenosis and carotid atherosclerotic plaque evaluation

Abstract

Objective: No new U-net (nnU-Net) is a newly-developed deep learning neural network, whose advantages in medical image segmentation have been noticed recently. This study aimed to investigate the value of the nnU-Net-based model for computed tomography angiography (CTA) imaging in assisting the evaluation of carotid artery stenosis (CAS) and atherosclerotic plaque. Methods: This study retrospectively enrolled 93 CAS-suspected patients who underwent head and neck CTA examination, then randomly divided them into the training set (N = 70) and the validation set (N = 23) in a 3:1 ratio. The radiologist-marked images in the training set were used for the development of the nnU-Net model, which was subsequently tested in the validation set. Results: In the training set, the nnU-Net had already displayed a good performance for CAS diagnosis and atherosclerotic plaque segmentation. Then, its utility was further confirmed in the validation set: the Dice similarity coefficient value of the nnU-Net model in segmenting background, blood vessels, calcification plaques, and dark spots reached 0.975, 0.974 0.795, and 0.498, accordingly. Besides, the nnU-Net model displayed a good consistency with physicians in assessing CAS (Kappa = 0.893), stenosis degree (Kappa = 0.930), the number of calcification plaque (Kappa = 0.922), non-calcification (Kappa = 0.768) and mixed plaque (Kappa = 0.793), as well as the max thickness of calcification plaque (intraclass correlation coefficient = 0.972). Additionally, the evaluation time of the nnU-Net model was shortened compared with the physicians (27.3 ± 4.4 s vs. 296.8 ± 81.1 s, p < 0.001). Conclusion: The automatic segmentation model based on nnU-Net shows good accuracy, reliability, and efficiency in assisting CTA to evaluate CAS and carotid atherosclerotic plaques.

Keywords: atherosclerotic plaque; automatic segmentation; carotid artery stenosis; computed tomography angiography; nnU-Net.

FIGURE 1

Study flow.

FIGURE 2

The nnU-Net model showed good consistency of atherosclerotic plaque number evaluation with physicians in the training set and validation set. The consistency between the nnU-Net model and physicians in evaluating the number of calcification plaque (A), non-calcification plaque (B), and mixed plaque (C) in the training set. The consistency between the nnU-Net model and physicians in evaluating the number of calcification plaque (D), non-calcification plaque (E), and mixed plaque (F) in the validation set.

FIGURE 3

The nnU-Net model showed good consistency of calcification plaque max thickness evaluation with physicians in the training set and validation set. The consistency between the nnU-Net model and physicians in evaluating the max thickness of calcification plaque in the training set (A) and the validation set (B).

FIGURE 4

The nnU-Net model took less time for CAS and atherosclerotic plaque evaluation compared with physicians. Comparison of the evaluation time between the nnU-Net and physicians in the training set (A) and the validation set (B).