PSAA-nnUNet: An Efficient Method for CT Carotid Artery Image Segmentation.

Zhaoxin Wang, Wenwen Yang, Dian Zhang, Lei Ma

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Article in Advances in experimental medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Zhaoxin WangSchool of Information Science and Technology, Nantong University, Nantong, Jiangsu Province, China.

Wenwen YangSchool of Information Science and Technology, Nantong University, Nantong, Jiangsu Province, China.

Dian ZhangSchool of Information Science and Technology, Nantong University, Nantong, Jiangsu Province, China.

Lei MaSchool of Information Science and Technology, Nantong University, Nantong, Jiangsu Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Carotid artery (CA) stenosis (CAS) constitutes a significant factor to ischaemic cerebrovascular events which exhibiting no overt symptoms in the early stages. Early detection of CAS can prevent ischaemic stroke and improve patient prognosis. In this study, we developed a non-invasive CAS automatic assessment method based on deep learning, intended for the early detection of CAS with CT imaging. The method proposed in this paper consists of three main components. First, we utilised thresholding and the Hessian-based Frangi filter to eliminate irrelevant tissue and enhance vascular structures. Second, we introduced a novel neural network named parameter shared axial attention (PSAA)-nnUNet for the automatic segmentation of CA. Finally, we assessed the degree of CAS with the North American Symptomatic Carotid Endarterectomy Trial (NASCET) formula. The PSAA-nnUNet algorithm proposed in this study achieved a segmentation accuracy of 0.82. The non-invasive CAS automatic assessment method based on PSAA-nnUNet exhibits excellent accuracy and great application potential.

Indexed as

Carotid ArteriesCarotid StenosisDeep LearningAlgorithmsComputed Tomography AngiographyHumansNeural Networks, ComputerRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedCarotid artery stenosis (CAS)Cerebral vascular accident (CVA)Dice similarity score (DSC)Ischaemic strokeNeural network

Identifiers

PMID39400833

DOI10.1016/j.acra.2018.12.005

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.