Shouhei Hanaoka1,2, Yoshitaka Masutani3, Mitsutaka Nemoto4, Yukihiro Nomura4, Soichiro Miki4, Takeharu Yoshikawa4, Naoto Hayashi4, Kuni Ohtomo5, Akinobu Shimizu6. 1. Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. hanaoka-tky@umin.ac.jp. 2. Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo, Japan. hanaoka-tky@umin.ac.jp. 3. Department of Biomedical Information Sciences, Hiroshima City University, Hiroshima, Japan. 4. Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan. 5. Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. 6. Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo, Japan.
Abstract
PURPOSE: A fully automatic multiatlas-based method for segmentation of the spine and pelvis in a torso CT volume is proposed. A novel landmark-guided diffeomorphic demons algorithm is used to register a given CT image to multiple atlas volumes. This algorithm can utilize both grayscale image information and given landmark coordinate information optimally. METHODS: The segmentation has four steps. Firstly, 170 bony landmarks are detected in the given volume. Using these landmark positions, an atlas selection procedure is performed to reduce the computational cost of the following registration. Then the chosen atlas volumes are registered to the given CT image. Finally, voxelwise label voting is performed to determine the final segmentation result. RESULTS: The proposed method was evaluated using 50 torso CT datasets as well as the public SpineWeb dataset. As a result, a mean distance error of [Formula: see text] and a mean Dice coefficient of [Formula: see text] were achieved for the whole spine and the pelvic bones, which are competitive with other state-of-the-art methods. CONCLUSION: From the experimental results, the usefulness of the proposed segmentation method was validated.
PURPOSE: A fully automatic multiatlas-based method for segmentation of the spine and pelvis in a torso CT volume is proposed. A novel landmark-guided diffeomorphic demons algorithm is used to register a given CT image to multiple atlas volumes. This algorithm can utilize both grayscale image information and given landmark coordinate information optimally. METHODS: The segmentation has four steps. Firstly, 170 bony landmarks are detected in the given volume. Using these landmark positions, an atlas selection procedure is performed to reduce the computational cost of the following registration. Then the chosen atlas volumes are registered to the given CT image. Finally, voxelwise label voting is performed to determine the final segmentation result. RESULTS: The proposed method was evaluated using 50 torso CT datasets as well as the public SpineWeb dataset. As a result, a mean distance error of [Formula: see text] and a mean Dice coefficient of [Formula: see text] were achieved for the whole spine and the pelvic bones, which are competitive with other state-of-the-art methods. CONCLUSION: From the experimental results, the usefulness of the proposed segmentation method was validated.
Authors: Jianhua Yao; Joseph E Burns; Daniel Forsberg; Alexander Seitel; Abtin Rasoulian; Purang Abolmaesumi; Kerstin Hammernik; Martin Urschler; Bulat Ibragimov; Robert Korez; Tomaž Vrtovec; Isaac Castro-Mateos; Jose M Pozo; Alejandro F Frangi; Ronald M Summers; Shuo Li Journal: Comput Med Imaging Graph Date: 2016-01-02 Impact factor: 4.790
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