TY - GEN
T1 - Segmentation of flexor tendons within carpal tunnel from magnetic resonance image
AU - Chen, Hsin Chen
AU - Sun, Yung-Nien
AU - Wang, Yi Ying
AU - Lin, Cheng Hsien
AU - Wang, Chien-Kuo
AU - Jou, I. Ming
PY - 2010/12/1
Y1 - 2010/12/1
N2 - Carpal tunnel syndrome (CTS) is one of the most common peripheral neuropathies. For evaluation of the CTS, segmentation of carpal tunnel and its contents from magnetic resonance (MR) images is rather important. In this paper we propose a new segmentation framework for the flexor tendons within the carpal tunnel on MR images. The proposed method consists of two stages including construction of reference tendon contour (RTC) and registration-based segmentation. Initially, we segment the flexor tendons from the most proximally cross-sectional image using watershed algorithm. The resulting segmented tendons then serve as the RTCs and are applied to the registration-based segmentation stage. Each of the RTCs in this stage is registered to its distally adjacent image by a rigid transformation and then its shape is refined by snake deformation. The registration-based segmentation is sequentially performed on each pair of adjacent cross-sectional images along the proximal to distal direction until the most distal image is segmented. Consequently, the flexor tendons in the entire MR volume can be automatically segmented. In the experiments three MR volumes were used to validate the accuracy of the proposed method. Compared to the manual results, our proposed method showed good agreement on the tendon segmentations with dice similarity coefficient above 0.8.
AB - Carpal tunnel syndrome (CTS) is one of the most common peripheral neuropathies. For evaluation of the CTS, segmentation of carpal tunnel and its contents from magnetic resonance (MR) images is rather important. In this paper we propose a new segmentation framework for the flexor tendons within the carpal tunnel on MR images. The proposed method consists of two stages including construction of reference tendon contour (RTC) and registration-based segmentation. Initially, we segment the flexor tendons from the most proximally cross-sectional image using watershed algorithm. The resulting segmented tendons then serve as the RTCs and are applied to the registration-based segmentation stage. Each of the RTCs in this stage is registered to its distally adjacent image by a rigid transformation and then its shape is refined by snake deformation. The registration-based segmentation is sequentially performed on each pair of adjacent cross-sectional images along the proximal to distal direction until the most distal image is segmented. Consequently, the flexor tendons in the entire MR volume can be automatically segmented. In the experiments three MR volumes were used to validate the accuracy of the proposed method. Compared to the manual results, our proposed method showed good agreement on the tendon segmentations with dice similarity coefficient above 0.8.
UR - http://www.scopus.com/inward/record.url?scp=79851473032&partnerID=8YFLogxK
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U2 - 10.1109/COMPSYM.2010.5685376
DO - 10.1109/COMPSYM.2010.5685376
M3 - Conference contribution
AN - SCOPUS:79851473032
SN - 9781424476404
T3 - ICS 2010 - International Computer Symposium
SP - 932
EP - 935
BT - ICS 2010 - International Computer Symposium
T2 - 2010 International Computer Symposium, ICS 2010
Y2 - 16 December 2010 through 18 December 2010
ER -