Dimitrios Damopoulos1, Till Dominic Lerch2, Florian Schmaranzer2, Moritz Tannast2, Christophe Chênes3, Guoyan Zheng4, Jérôme Schmid3. 1. Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014, Bern, Switzerland. dimitrios.damopoulos@istb.unibe.ch. 2. Department of Orthopaedic Surgery and Traumatology, Inselspital, University of Bern, Freiburgstrasse, 3010, Bern, Switzerland. 3. School of Health Sciences - Geneva, HES-SO University of Applied Sciences and Arts Western Switzerland, Avenue de Champel 47, 1206, Geneva, Switzerland. 4. Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014, Bern, Switzerland. guoyan.zheng@istb.unibe.ch.
Abstract
BACKGROUND: Radial 2D MRI scans of the hip are routinely used for the diagnosis of the cam type of femoroacetabular impingement (FAI) and of avascular necrosis (AVN) of the femoral head, both considered causes of hip joint osteoarthritis in young and active patients. A method for automated and accurate segmentation of the proximal femur from radial MRI scans could be very useful in both clinical routine and biomechanical studies. However, to our knowledge, no such method has been published before. PURPOSE: The aims of this study are the development of a system for the segmentation of the proximal femur from radial MRI scans and the reconstruction of its 3D model that can be used for diagnosis and planning of hip-preserving surgery. METHODS: The proposed system relies on: (a) a random forest classifier and (b) the registration of a 3D template mesh of the femur to the radial slices based on a physically based deformable model. The input to the system are the radial slices and the manually specified positions of three landmarks. Our dataset consists of the radial MRI scans of 25 patients symptomatic of FAI or AVN and accompanying manual segmentation of the femur, treated as the ground truth. RESULTS: The achieved segmentation of the proximal femur has an average Dice similarity coefficient (DSC) of 96.37 ± 1.55%, an average symmetric mean absolute distance (SMAD) of 0.94 ± 0.39 mm and an average Hausdorff distance of 2.37 ± 1.14 mm. In the femoral head subregion, the average SMAD is 0.64 ± 0.18 mm and the average Hausdorff distance is 1.41 ± 0.56 mm. CONCLUSIONS: We validated a semiautomated method for the segmentation of the proximal femur from radial MR scans. A 3D model of the proximal femur is also reconstructed, which can be used for the planning of hip-preserving surgery.
BACKGROUND: Radial 2D MRI scans of the hip are routinely used for the diagnosis of the cam type of femoroacetabular impingement (FAI) and of avascular necrosis (AVN) of the femoral head, both considered causes of hip joint osteoarthritis in young and active patients. A method for automated and accurate segmentation of the proximal femur from radial MRI scans could be very useful in both clinical routine and biomechanical studies. However, to our knowledge, no such method has been published before. PURPOSE: The aims of this study are the development of a system for the segmentation of the proximal femur from radial MRI scans and the reconstruction of its 3D model that can be used for diagnosis and planning of hip-preserving surgery. METHODS: The proposed system relies on: (a) a random forest classifier and (b) the registration of a 3D template mesh of the femur to the radial slices based on a physically based deformable model. The input to the system are the radial slices and the manually specified positions of three landmarks. Our dataset consists of the radial MRI scans of 25 patients symptomatic of FAI or AVN and accompanying manual segmentation of the femur, treated as the ground truth. RESULTS: The achieved segmentation of the proximal femur has an average Dice similarity coefficient (DSC) of 96.37 ± 1.55%, an average symmetric mean absolute distance (SMAD) of 0.94 ± 0.39 mm and an average Hausdorff distance of 2.37 ± 1.14 mm. In the femoral head subregion, the average SMAD is 0.64 ± 0.18 mm and the average Hausdorff distance is 1.41 ± 0.56 mm. CONCLUSIONS: We validated a semiautomated method for the segmentation of the proximal femur from radial MR scans. A 3D model of the proximal femur is also reconstructed, which can be used for the planning of hip-preserving surgery.
Entities:
Keywords:
3D reconstruction; Deformable model; Proximal femur; Radial imaging of the hip; Random forest; Segmentation
Authors: Xianjin Dai; Yang Lei; Tonghe Wang; Jun Zhou; Soumon Rudra; Mark McDonald; Walter J Curran; Tian Liu; Xiaofeng Yang Journal: Phys Med Biol Date: 2022-01-21 Impact factor: 3.609
Authors: Till D Lerch; Florian Schmaranzer; Markus S Hanke; Christiane Leibold; Simon D Steppacher; Klaus A Siebenrock; Moritz Tannast Journal: Orthopade Date: 2020-06 Impact factor: 1.087
Authors: Till D Lerch; Sébastien Zwingelstein; Florian Schmaranzer; Adam Boschung; Markus S Hanke; Inga A S Todorski; Simon D Steppacher; Nicolas Gerber; Guodong Zeng; Klaus A Siebenrock; Moritz Tannast Journal: Orthop J Sports Med Date: 2021-05-28
Authors: Neeraja Konuthula; Francisco A Perez; A Murat Maga; Waleed M Abuzeid; Kris Moe; Blake Hannaford; Randall A Bly Journal: Int J Comput Assist Radiol Surg Date: 2021-05-19 Impact factor: 3.421
Authors: Guodong Zeng; Florian Schmaranzer; Celia Degonda; Nicolas Gerber; Kate Gerber; Moritz Tannast; Jürgen Burger; Klaus A Siebenrock; Guoyan Zheng; Till D Lerch Journal: Eur J Radiol Open Date: 2020-12-18
Authors: Steven F DeFroda; Thomas D Alter; Floor Lambers; Philip Malloy; Ian M Clapp; Jorge Chahla; Shane J Nho Journal: Orthop J Sports Med Date: 2021-11-19
Authors: Guodong Zeng; Celia Degonda; Adam Boschung; Florian Schmaranzer; Nicolas Gerber; Klaus A Siebenrock; Simon D Steppacher; Moritz Tannast; Till D Lerch Journal: Orthop J Sports Med Date: 2021-11-24
Authors: Nastassja Pamela Ewertowski; Christoph Schleich; Daniel Benjamin Abrar; Harish S Hosalkar; Bernd Bittersohl Journal: J Orthop Surg Res Date: 2022-07-30 Impact factor: 2.677