Segmentation of cam-type femurs from CT scans

We introduce a new way to accurately segment cam-type pathological femurs from pelvic CT scans. The femur is a difficult target for segmentation due to its proximity to the acetabulum, irregular shape and the varying thickness of its hardened outer shell. In addition, the pathological femurs with femoral-acetabular impingements have a non-standard shape, which increases segmentation difficulty. We overcome these difficulties by (a) dividing the femur into two rounds of segmentation—one for the femur head and another for the body—(b) pre-processing the CT scan to reduce anatomical sources of error (c) two modes of segmentation—a rough estimation of a contour and another for fine contours. Segmentations of the CT volume are performed iteratively, on a slice-by-slice basis and contours are extracted using the morphological snake algorithm. Our methodology was designed to require little initialization from the user and to deftly handle the large variation in femur shapes, most notably from deformations attributed to cam femoral–acetabular impingements. Our efforts are to provide physicians with a new tool that creates patient-specific and high-quality 3D femur models while requiring much less time and effort. Femur models segmented with our method had an average volume overlap error of 2.71±0.44% and symmetric surface distance of 0.28±0.04 mm compared to ground truth models.

[1]  Jürgen Weese,et al.  Automated 3-D PDM construction from segmented images using deformable models , 2003, IEEE Transactions on Medical Imaging.

[2]  N. Magnenat-Thalmann,et al.  HIP JOINT RECONSTRUCTION AND MOTION VISUALIZATION USING MRI AND OPTICAL MOTION CAPTURE , 2003 .

[3]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[4]  Xun Wang,et al.  A comparative study of deformable contour methods on medical image segmentation , 2008, Image Vis. Comput..

[5]  Demetri Terzopoulos,et al.  T-snakes: Topology adaptive snakes , 2000, Medical Image Anal..

[6]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Luis Álvarez,et al.  A Real Time Morphological Snakes Algorithm , 2012, Image Process. Line.

[8]  M. Tannast,et al.  Femoroacetabular impingement: radiographic diagnosis--what the radiologist should know. , 2007, AJR. American journal of roentgenology.

[9]  Timothy F. Cootes,et al.  Face recognition using the active appearance model. , 1998, European Conference on Computer Vision.

[10]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[11]  Manuel Menezes de Oliveira Neto,et al.  Real-time line detection through an improved Hough transform voting scheme , 2008, Pattern Recognit..

[12]  G W Sherouse,et al.  Computation of digitally reconstructed radiographs for use in radiotherapy treatment design. , 1990, International journal of radiation oncology, biology, physics.

[13]  Luis Álvarez,et al.  Morphological snakes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Siqi Chen,et al.  Level set segmentation with both shape and intensity priors , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  Simon Stegmaier,et al.  A simple and flexible volume rendering framework for graphics-hardware-based raycasting , 2005, Fourth International Workshop on Volume Graphics, 2005..

[16]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[17]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[18]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .

[19]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[20]  Hideki Yoshikawa,et al.  Automated segmentation of acetabulum and femoral head from 3-d CT images , 2003, IEEE Transactions on Information Technology in Biomedicine.

[21]  Thomas B. Moeslund,et al.  Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Daniel Cremers,et al.  Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation , 2006, International Journal of Computer Vision.

[23]  Nadia Magnenat-Thalmann,et al.  Anatomical Modelling of the Musculoskeletal System from MRI , 2006, MICCAI.

[24]  Jun Han,et al.  Model-Based Segmentation of Femoral Head and Acetabulum from CT Images , 2007, 2007 IEEE/ICME International Conference on Complex Medical Engineering.

[25]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[27]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[28]  M. Hossain,et al.  Current management of femoro-acetabular impingement , 2008 .

[29]  Daniel Cremers,et al.  Diffusion-Snakes Using Statistical Shape Knowledge , 2000, AFPAC.

[30]  Nadia Magnenat-Thalmann,et al.  Robust statistical shape models for MRI bone segmentation in presence of small field of view , 2011, Medical Image Anal..

[31]  Yoshinobu Sato,et al.  Automated Segmentation of the Femur and Pelvis from 3D CT Data of Diseased Hip Using Hierarchical Statistical Shape Model of Joint Structure , 2009, MICCAI.

[32]  D CohenLaurent On active contour models and balloons , 1991 .