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A Knowledge-Based Approach for Carpal Tunnel Segmentation from Magnetic Resonance Images

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Abstract

Carpal tunnel syndrome (CTS) has been reported as one of the most common peripheral neuropathies. Carpal tunnel segmentation from magnetic resonance (MR) images is important for the evaluation of CTS. To date, manual segmentation, which is time-consuming and operator dependent, remains the most common approach for the analysis of the carpal tunnel structure. Therefore, we propose a new knowledge-based method for automatic segmentation of the carpal tunnel from MR images. The proposed method first requires the segmentation of the carpal tunnel from the most proximally cross-sectional image. Three anatomical features of the carpal tunnel are detected by watershed and polygonal curve fitting algorithms to automatically initialize a deformable model as close to the carpal tunnel in the given image as possible. The model subsequently deforms toward the tunnel boundary based on image intensity information, shape bending degree, and the geometry constraints of the carpal tunnel. After the deformation process, the carpal tunnel in the most proximal image is segmented and subsequently applied to a contour propagation step to extract the tunnel contours sequentially from the remaining cross-sectional images. MR volumes from 15 subjects were included in the validation experiments. Compared with the ground truth of two experts, our method showed good agreement on tunnel segmentations by an average margin of error within 1 mm and dice similarity coefficient above 0.9.

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Acknowledgments

The authors would like to express their appreciation for the grant under contract NSC 99-2627-B-006-010 from the National Science Council, Taiwan, ROC. This work also utilized the shared facilities supported by the Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan, ROC.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan, 701, Taiwan, Republic of China

    Hsin-Chen Chen & Yung-Nien Sun

  2. Hermes Microvision Inc., 7F., No.18, Puding Rd., East Dist., Hsinchu City, 300, Taiwan, Republic of China

    Yi-Ying Wang

  3. Flight Testing Division, Chung-Shan Institute of Science and Technology, Longtan, Taiwan, Republic of China

    Cheng-Hsien Lin

  4. Department of Radiology, National Cheng Kung University Hospital, No. 138, Sheng Li Road, Tainan, 704, Taiwan, Republic of China

    Chien-Kuo Wang

  5. Department of Orthopedics, College of Medicine, National Cheng Kung University, No. 138, Sheng Li Road, Tainan, 704, Taiwan, Republic of China

    I-Ming Jou

  6. Institute of Biomedical Engineering, National Cheng Kung University, No. 1, University Road, Tainan, 701, Taiwan, Republic of China

    Fong-Chin Su

  7. Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan, Republic of China

    Hsin-Chen Chen, I-Ming Jou, Fong-Chin Su & Yung-Nien Sun

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  1. Hsin-Chen Chen
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Correspondence to Yung-Nien Sun.

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Chen, HC., Wang, YY., Lin, CH. et al. A Knowledge-Based Approach for Carpal Tunnel Segmentation from Magnetic Resonance Images. J Digit Imaging 26, 510–520 (2013). https://doi.org/10.1007/s10278-012-9530-2

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  • DOI: https://doi.org/10.1007/s10278-012-9530-2

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