A knowledge-based approach for carpal tunnel segmentation from magnetic resonance images

Hsin Chen Chen, Yi Ying Wang, Cheng Hsien Lin, Chien-Kuo Wang, I. Ming Jou, Fong-chin Su, Yung-Nien Sun

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)510-520
Number of pages11
JournalJournal of Digital Imaging
Volume26
Issue number3
DOIs
Publication statusPublished - 2013 Jun 1

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Magnetic resonance
Wrist
Tunnels
Magnetic Resonance Spectroscopy
Carpal Tunnel Syndrome
Peripheral Nervous System Diseases
Curve fitting
Watersheds

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

Cite this

Chen, Hsin Chen ; Wang, Yi Ying ; Lin, Cheng Hsien ; Wang, Chien-Kuo ; Jou, I. Ming ; Su, Fong-chin ; Sun, Yung-Nien. / A knowledge-based approach for carpal tunnel segmentation from magnetic resonance images. In: Journal of Digital Imaging. 2013 ; Vol. 26, No. 3. pp. 510-520.
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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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A knowledge-based approach for carpal tunnel segmentation from magnetic resonance images. / Chen, Hsin Chen; Wang, Yi Ying; Lin, Cheng Hsien; Wang, Chien-Kuo; Jou, I. Ming; Su, Fong-chin; Sun, Yung-Nien.

In: Journal of Digital Imaging, Vol. 26, No. 3, 01.06.2013, p. 510-520.

Research output: Contribution to journalArticle

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