Deep CTS: a Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome.
Carpal tunnel syndrome (CTS) is a common peripheral nerve disease in adults; it can cause pain, numbness, and even muscle atrophy and will adversely affect patients' daily life and work. There are no standard diagnostic criteria that go against the early diagnosis and treatment of patients. MRI as a...
| Published in: | Journal of Digital Imaging Vol. 35; no. 6; pp. 1433 - 1445 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160503241&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160503241 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2022 vid: 35 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160503241 157211553 160503241 160503241 10.1007/s10278-022-00661-4 160503241 ppf: 1433 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep CTS: a Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome. aug: au: Zhou, Haiying Bai, Qi Hu, Xianliang Alhaskawi, Ahmad Dong, Yanzhao Wang, Zewei Qi, Binjie Fang, Jianyong Kota, Vishnu Goutham Abdulla, Mohamed Hasan Abdulla Hasa Ezzi, Sohaib Hasan Abdullah Lu, Hui affil: Department of Orthopedics, College of Medicine, The First Affiliated Hospital, Zhejiang University, #79 Qingchun Road, 310003, Hangzhou, Zhejiang Province, People's Republic of China sug: subj: Deep Learning Neural Networks (Computer) Magnetic Resonance Imaging Image Processing, Computer Assisted Carpal Tunnel Syndrome Diagnosis Human Artifacts Automation Algorithms Sensitivity and Specificity ab: Carpal tunnel syndrome (CTS) is a common peripheral nerve disease in adults; it can cause pain, numbness, and even muscle atrophy and will adversely affect patients' daily life and work. There are no standard diagnostic criteria that go against the early diagnosis and treatment of patients. MRI as a novel imaging technique can show the patient's condition more objectively, and several characteristics of carpal tunnel syndrome have been found. However, various image sequences, heavy artifacts, small lesion characteristics, high volume of imagine reading, and high difficulty in MRI interpretation limit its application in clinical practice. With the development of automatic image segmentation technology, the algorithm has great potential in medical imaging. The challenge is that the segmentation target is too small, and there are two categories of images with the proximal border of the carpal tunnel as the boundary. To meet the challenge, we propose an end-to-end deep learning framework called Deep CTS to segment the carpal tunnel from the MR image. The Deep CTS consists of the shape classifier with a simple convolutional neural network and the carpal tunnel region segmentation with simplified U-Net. With the specialized structure for the carpal tunnel, Deep CTS can segment the carpal tunnel region efficiently and improve the intersection over union of results. The experimental results demonstrated that the performance of the proposed deep learning framework is better than other segmentation networks for small objects. We trained the model with 333 images, tested it with 82 images, and achieved 0.63 accuracy of intersection over union and 0.17 s segmentation efficiency, which indicate great promise for the clinical application of this algorithm. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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