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Deep CTS: a Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome

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Abstract

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.

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Availability of Data and Materials

The dataset supporting the conclusions of this article is included with the article.

Abbreviations

CTS:

Carpal tunnel syndrome

EMG:

Electromyography

NCS:

Nerve conduction study

MRI:

Magnetic resonance image

CNN:

Convolutional neural networks

FCN:

Fully convolutional networks

FOV:

Field of view

CLAHE:

Contrast Limited Adaptive Histogram Equalization

2D:

Two-dimensional

BN:

Batch normalization

ReLU:

Rectified linear unit

TP:

True positive

TN:

True negative

FP:

False positive

FN:

False negative

PA:

Pixel accuracy

IoU:

Intersection over union.

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Acknowledgements

This work was supported by Alibaba Cloud.

Funding

The study was funded by the National Natural Science Foundation of China (grant number 81702135), Zhejiang Provincial Natural Science Foundation (grant number LY20H060007, LS21H060001), Zhejiang Traditional Chinese Medicine Research Program (grant number 2017ZB057), and Alibaba Youth Studio Project (the grant number ZJU-032). The funding bodies had no role in the design of the study; in collection, analysis, and interpretation of data; and in drafting the manuscript.

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Authors and Affiliations

  1. Department of Orthopedics, College of Medicine, The First Affiliated Hospital, Zhejiang University, #79 Qingchun Road, Hangzhou, Zhejiang Province, People’s Republic of China, 310003

    Haiying Zhou, Ahmad Alhaskawi, Yanzhao Dong & Hui Lu

  2. Department of Rehabilitation Medicine, College of Medicine, The First Affiliated Hospital, Zhejiang University, #79 Qingchun Road, Hangzhou, Zhejiang Province, People’s Republic of China, 310003

    Binjie Qi

  3. School of Mathematical Sciences, Zhejiang University, #38 Zheda Road, Hangzhou, Zhejiang Province, People’s Republic of China, 310027

    Qi Bai & **anliang Hu

  4. Suzhou Warrior Pioneer Software Co., Ltd, Room 26, Building 17, No. 6, Trade City, Wuzhong Economic Development Zone, Suzhou, Jiangsu Province, People’s Republic of China, 215000

    Jianyong Fang

  5. Zhejiang University School of Medicine, #866 Yuhangtang Road, Hangzhou, Zhejiang Province, People’s Republic of China, 3100058

    Zewei Wang, Vishnu Goutham Kota, Mohamed Hasan Abdulla Hasa Abdulla & Sohaib Hasan Abdullah Ezzi

  6. Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, #866 Yuhangtang Road, Hangzhou, Zhejiang Province, People’s Republic of China, 310058

    Hui Lu

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  1. Haiying Zhou
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Contributions

HL designed the study; HY Z, AA, YZ D, and QJ B performed data collection; QB, XL H, and JY F analyzed the results; and MHAHA, SHAE, VGK, and ZW W drafted the manuscript. The authors have read and approved the final manuscript.

Corresponding author

Correspondence to Hui Lu.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study protocols were approved by the Medical Ethics Committee of the First Affiliated Hospital of the College of Medicine, Zhejiang University (ethics approval number: 2021(224)).

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Written informed consent was obtained from the patient for publication of clinical details and clinical images. Upon request, a copy of the consent form is available for review by the editor of this journal.

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Zhou, H., Bai, Q., Hu, X. et al. Deep CTS: a Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome. J Digit Imaging 35, 1433–1444 (2022). https://doi.org/10.1007/s10278-022-00661-4

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