The application of the nnU-Net-based automatic segmentation model in assisting carotid artery stenosis and carotid atherosclerotic plaque evaluation - Zhu, Ying; Chen, Liwei; Lu, Wenjie; Gong, Yongjun; Wang, Ximing (Frontiers in Physiology, 2022)
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 seconds vs. 296.8±81.1 seconds, 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.