Automatic femur segmentation and condyle line detection in 3D MR scans for alignment of high resolution MR

Jolly, Marie-Pierre; Alvino, Christopher; Odry, Benjamin L.; Deng, Xiang; Zheng, Jiangbin; Harder, Martin; Guehring, Jens

doi:10.1109/isbi.2010.5490142

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…It has recently been demonstrated that 1.5 T MRI can generate accurate 3D proximal femoral models with absolute agreement when compared to the clinical gold standard of CT derived models and laser scanner ground truth models of excised femurs 25 . Since 1.5 T MRI scanners are widely available in the orthopedic clinical setting, and advancements in the artificial intelligence field are making the segmentation of bone from MRI images an automatic and quicker process, thus easier for widespread clinical use, a shift towards minimizing or eliminating ionizing radiation exposure in the hip preservation setting is warranted 34–42 . Additionally, the ability of MRI to assess multiple tissues with a single imaging modality, streamlines the preoperative workup by reducing the burden of multiple diagnostic imaging tests for patients.…”

Section: Introductionmentioning

confidence: 99%

“…25 Since 1.5 T MRI scanners are widely available in the orthopedic clinical setting, and advancements in the artificial intelligence field are making the segmentation of bone from MRI images an automatic and quicker process, thus easier for widespread clinical use, a shift towards minimizing or eliminating ionizing radiation exposure in the hip preservation setting is warranted. [34][35][36][37][38][39][40][41][42] Additionally, the ability of MRI to assess multiple tissues with a single imaging modality, streamlines the preoperative workup by reducing the burden of multiple diagnostic imaging tests for patients. The use of MRI also opens the field for opportunities to generate new metrics to accurately estimate the 3D nature of bony deformities.…”

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confidence: 99%

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MRI‐ and CT‐based metrics for the quantification of arthroscopic bone resections in femoroacetabular impingement syndrome

Guidetti

Malloy

Alter

et al. 2021

Journal Orthopaedic Research

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The purpose of this in vitro study was to quantify the bone resected from the proximal femur during hip arthroscopy using metrics generated from magnetic resonance imaging (MRI) and computed tomography (CT) reconstructed threedimensional (3D) bone models. Seven cadaveric hemipelvises underwent both a 1.5 T MRI and CT scan before and following an arthroscopic proximal femoral osteochondroplasty. The images from MRI and CT were segmented to generate 3D proximal femoral surface models. A validated 3D-3D registration method was used to compare surface-to-surface distances between the 3D models before and following surgery. The new metrics of maximum height, mean height, surface area and volume, were computed to quantify bone resected during osteochondroplasty.Stability of the metrics across imaging modalities was established through paired sample t-tests and bivariate correlation. Bivariate correlation analyses indicated strong correlations between all metrics (r = 0.728-0.878) computed from MRI and CT derived models. There were no differences in the MRI-and CT-based metrics used to quantify bone resected during femoral osteochondroplasty. Preoperative and postoperative MRI and CT derived 3D bone models can be used to quantify bone resected during femoral osteochondroplasty, without significant differences between the imaging modalities.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

MRI‐ and CT‐based metrics for the quantification of arthroscopic bone resections in femoroacetabular impingement syndrome

Guidetti

Malloy

Alter

et al. 2021

Journal Orthopaedic Research

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“…The study is not without limitations. We expect some level of inter‐subject variability that could be improved with the automation of the MRI segmentation process and of the shape‐fitting method 56–58 . The shape‐fitting method lends itself to be automated using applications of machine learning and neural networks such as automatic landmark localization using convolutional neural network classifiers and shape statistics, 59,60 learning‐based stochastic object model 61 and machine learning and neural networks for predictions, including regression, classification, and anomaly detection.…”

Section: Discussionmentioning

confidence: 99%

Noninvasive shape‐fitting method quantifies cam morphology in femoroacetabular impingement syndrome: Implications for diagnosis and surgical planning

Guidetti

Malloy

Alter

et al. 2022

Journal Orthopaedic Research

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There are considerable limitations associated with the standard 2D imaging currently used for the diagnosis and surgical planning of cam-type femoroacetabular impingement syndrome (FAIS). The aim of this study was to determine the accuracy of a new patient-specific shape-fitting method that quantifies cam morphology in 3D based solely on preoperative MRI imaging. Preoperative and postoperative 1.5T MRI scans were performed on n = 15 patients to generate 3D models of the proximal femur, in turn used to create the actual and the virtual cam. The actual cams were reconstructed by subtracting the postoperative from the preoperative 3D model and used as reference, while the virtual cams were generated by subtracting the preoperative 3D model from the virtual shape template produced with the shape-fitting method based solely on preoperative MRI scans. The accuracy of the shape-fitting method was tested on all patients by evaluating the agreement between the metrics of height, surface area, and volume that quantified virtual and actual cams. Accuracy of the shape-fitting method was demonstrated obtaining a 97.8% average level of agreement between these metrics. In conclusion, the shape-fitting technique is a noninvasive and patientspecific tool for the quantification and localization of cam morphology. Future studies will include the implementation of the technique within a clinically based software for diagnosis and surgical planning for cam-type FAIS.

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“…Bystrov et al 4 derive anatomical landmarks by adapting an active shape model to a 3D scout scan and use the obtained landmarks for defining the field of view. This method has been clinically evaluated by Lecouvet et al 15 Jolly et al 12 took a different approach. They did not apply a model-based technique, but employ a combination of hidden Markov models and random walker algorithm for segmentation and condyle detection on 3D scout images instead.…”

Section: Related Workmentioning

confidence: 99%

Automatic Scan Planning for Magnetic Resonance Imaging of the Knee Joint

et al. 2012

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Automatic scan planning for magnetic resonance imaging of the knee aims at defining an oriented bounding box around the knee joint from sparse scout images in order to choose the optimal field of view for the diagnostic images and limit acquisition time. We propose a fast and fully automatic method to perform this task based on the standard clinical scout imaging protocol. The method is based on sequential Chamfer matching of 2D scout feature images with a three-dimensional mean model of femur and tibia. Subsequently, the joint plane separating femur and tibia, which contains both menisci, can be automatically detected using an information-augmented active shape model on the diagnostic images. This can assist the clinicians in quickly defining slices with standardized and reproducible orientation, thus increasing diagnostic accuracy and also comparability of serial examinations. The method has been evaluated on 42 knee MR images. It has the potential to be incorporated into existing systems because it does not change the current acquisition protocol.

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Automatic femur segmentation and condyle line detection in 3D MR scans for alignment of high resolution MR

Cited by 6 publications

References 7 publications

MRI‐ and CT‐based metrics for the quantification of arthroscopic bone resections in femoroacetabular impingement syndrome

MRI‐ and CT‐based metrics for the quantification of arthroscopic bone resections in femoroacetabular impingement syndrome

Noninvasive shape‐fitting method quantifies cam morphology in femoroacetabular impingement syndrome: Implications for diagnosis and surgical planning

Automatic Scan Planning for Magnetic Resonance Imaging of the Knee Joint

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Automatic femur segmentation and condyle line detection in 3D MR scans for alignment of high resolution MR

Cited by 6 publications

References 7 publications

MRI‐­ and CT‐­based metrics for the quantification of arthroscopic bone resections in femoroacetabular impingement syndrome

MRI‐­ and CT‐­based metrics for the quantification of arthroscopic bone resections in femoroacetabular impingement syndrome

Noninvasive shape‐fitting method quantifies cam morphology in femoroacetabular impingement syndrome: Implications for diagnosis and surgical planning

Automatic Scan Planning for Magnetic Resonance Imaging of the Knee Joint

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MRI‐ and CT‐based metrics for the quantification of arthroscopic bone resections in femoroacetabular impingement syndrome

MRI‐ and CT‐based metrics for the quantification of arthroscopic bone resections in femoroacetabular impingement syndrome