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

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Author(s): Ying Zhu ¹ , Liwei Chen ² , Wenjie Lu ² , Yongjun Gong ² ^, ^* ^, , Ximing Wang ³ ^, ^* ^,

Publication date (Electronic): 06 December 2022

Journal: Frontiers in Physiology

Publisher: Frontiers Media S.A.

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

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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.

Related collections

Most cited references 33

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nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Fabian Isensee, Paul Jaeger, Simon Kohl … (2020)

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.

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Fully Convolutional Networks for Semantic Segmentation.

Trevor Darrell, Evan Shelhamer, Jonathan Long (2017)

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional networks achieve improved segmentation of PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image.

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Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis.

H Barnett, D W Taylor, R B Haynes … (1991)

Without strong evidence of benefit, the use of carotid endarterectomy for prophylaxis against stroke rose dramatically until the mid-1980s, then declined. Our investigation sought to determine whether carotid endarterectomy reduces the risk of stroke among patients with a recent adverse cerebrovascular event and ipsilateral carotid stenosis. We conducted a randomized trial at 50 clinical centers throughout the United States and Canada, in patients in two predetermined strata based on the severity of carotid stenosis--30 to 69 percent and 70 to 99 percent. We report here the results in the 659 patients in the latter stratum, who had had a hemispheric or retinal transient ischemic attack or a nondisabling stroke within the 120 days before entry and had stenosis of 70 to 99 percent in the symptomatic carotid artery. All patients received optimal medical care, including antiplatelet therapy. Those assigned to surgical treatment underwent carotid endarterectomy performed by neurosurgeons or vascular surgeons. All patients were examined by neurologists 1, 3, 6, 9, and 12 months after entry and then every 4 months. End points were assessed by blinded, independent case review. No patient was lost to follow-up. Life-table estimates of the cumulative risk of any ipsilateral stroke at two years were 26 percent in the 331 medical patients and 9 percent in the 328 surgical patients--an absolute risk reduction (+/- SE) 17 +/- 3.5 percent (P less than 0.001). For a major or fatal ipsilateral stroke, the corresponding estimates were 13.1 percent and 2.5 percent--an absolute risk reduction of 10.6 +/- 2.6 percent (P less than 0.001). Carotid endarterectomy was still found to be beneficial when all strokes and deaths were included in the analysis (P less than 0.001). Carotid endarterectomy is highly beneficial to patients with recent hemispheric and retinal transient ischemic attacks or nondisabling strokes and ipsilateral high-grade stenosis (70 to 99 percent) of the internal carotid artery.

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Author and article information

Contributors

Ximing Wang: URI : https://loop.frontiersin.org/people/2028950/overview

Journal

Journal ID (nlm-ta): Front Physiol

Journal ID (iso-abbrev): Front Physiol

Journal ID (publisher-id): Front. Physiol.

Title: Frontiers in Physiology

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-042X

Publication date (Electronic): 06 December 2022

Publication date Collection: 2022

Volume: 13

Electronic Location Identifier: 1057800

Affiliations

[1] ¹ First Clinical Medical College , Soochow University , Suzhou, China

[2] ² Department of Radiology , School of Medicine , Tongren Hospital , Shanghai Jiao Tong University , Shanghai, China

[3] ³ Department of Radiology , The First Affiliated Hospital of Soochow University , Suzhou, China

Author notes

Edited by: Dalin Tang, Worcester Polytechnic Institute, United States

Reviewed by: Xiaoya Guo, Nanjing University of Posts and Telecommunications, China

Seung Jaeseok, Seoul National University Hospital, South Korea

Huchuan Zhou, Army Medical University, China

*Correspondence: Yongjun Gong, GYJ2854@ 123456shtrhospital.com ; Ximing Wang, wangximing1998@ 123456163.com

[ † ]

These authors have contributed equally to this work

This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Physiology

Article

Publisher ID: 1057800

DOI: 10.3389/fphys.2022.1057800

PMC ID: 9763590

PubMed ID: 36561211

SO-VID: d6cbb299-5b85-44b5-8523-80311db88396

Copyright © Copyright © 2022 Zhu, Chen, Lu, Gong and Wang.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 08 October 2022

Date accepted : 11 November 2022

Categories

Subject: Physiology

Subject: Original Research

ScienceOpen disciplines: Anatomy & Physiology

Keywords: nnu-net,automatic segmentation,computed tomography angiography,carotid artery stenosis,atherosclerotic plaque

Data availability:

ScienceOpen disciplines: Anatomy & Physiology

Keywords: nnu-net, automatic segmentation, computed tomography angiography, carotid artery stenosis, atherosclerotic plaque

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