Lassad Ben Younes1, Yoshikazu Nakajima, Toki Saito. 1. Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan, benyounes@image.t.u-tokyo.ac.jp.
Abstract
PURPOSE: Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM. METHOD: Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting. RESULTS: The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm. CONCLUSION: The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.
PURPOSE: Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM. METHOD: Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting. RESULTS: The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm. CONCLUSION: The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.
Authors: Banchong Mahaisavariya; Kriskrai Sitthiseripratip; Trongtum Tongdee; Erik L J Bohez; Jos Vander Sloten; Philip Oris Journal: Med Eng Phys Date: 2002-11 Impact factor: 2.242
Authors: Manjori Rao; Joshua Stough; Yueh-Yun Chi; Keith Muller; Gregg Tracton; Stephen M Pizer; Edward L Chaney Journal: Int J Radiat Oncol Biol Phys Date: 2005-03-01 Impact factor: 7.038
Authors: Melissa R Requist; Yantarat Sripanich; Andrew C Peterson; Tim Rolvien; Alexej Barg; Amy L Lenz Journal: Int J Comput Assist Radiol Surg Date: 2021-02-19 Impact factor: 2.924