ArticleInternational journal of computer assisted radiology and surgery2019
Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 33 citations in OpenAlex.
- Magnetic Resonance Imaging-Based 3-Dimensional Models of the Pelvis and Hip Using Machine Learning for Automatic Bone Segmentation in a Dynamic Hip Impingement Simulation.Orthopaedic journal of sports medicine · 2025Article
- A Two stage deep learning network for automated femoral segmentation in bilateral lower limb CT scans.Scientific reports · 2025Article
- Using 2D U-Net convolutional neural networks for automatic acetabular and proximal femur segmentation of hip MRI images and morphological quantification: a preliminary study in DDH.Biomedical engineering online · 2024Article
- Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images.Quantitative imaging in medicine and surgery · 2022Article
- Automated measurement of alpha angle on 3D-magnetic resonance imaging in femoroacetabular impingement hips: a pilot study.Journal of orthopaedic surgery and research · 2022Article
- Multi-organ auto-delineation in head-and-neck MRI for radiation therapy using regional convolutional neural network.Physics in medicine and biology · 2022Article
- Three-Dimensional Magnetic Resonance Imaging Bone Models of the Hip Joint Using Deep Learning: Dynamic Simulation of Hip Impingement for Diagnosis of Intra- and Extra-articular Hip Impingement.Orthopaedic journal of sports medicine · 2021Article
- Quantification of Acetabular Coverage on 3-Dimensional Reconstructed Computed Tomography Scan Bone Models in Patients With Femoroacetabular Impingement Syndrome: A Descriptive Study.Orthopaedic journal of sports medicine · 2021Article
- Automated atlas-based segmentation for skull base surgical planning.International journal of computer assisted radiology and surgery · 2021Article
- Posterior Extra-articular Ischiofemoral Impingement Can Be Caused by the Lesser and Greater Trochanter in Patients With Increased Femoral Version: Dynamic 3D CT-Based Hip Impingement Simulation of a Modified FABER Test.Orthopaedic journal of sports medicine · 2021Article
- MRI-based 3D models of the hip joint enables radiation-free computer-assisted planning of periacetabular osteotomy for treatment of hip dysplasia using deep learning for automatic segmentation.European journal of radiology open · 2021Article
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7 authors at 2 institutions in 1 country.
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Abstract
backgroundRadial 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.
methodsThe 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.
resultsThe 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.
conclusionsWe 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.
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