Subject Specific Computational Models of the Knee to Predict Anterior Cruciate Ligament Injury

Knee joint is a complex joint involving multiple interactions between cartilage, bone, muscles, ligaments, tendons and neural control. Anterior Cruciate Ligament (ACL) is one ligament in the knee joint that frequently gets injured during various sports or recreational activities. ACL injuries are common in college level and professional athletes especially in females and the injury rate is growing in epidemic proportions despite significant increase in the research focusing on neuromuscular and proprioceptive training programs. Most ACL injuries lead to surgical reconstruction followed by a lengthy rehabilitation program impacting the health and performance of the athlete. Furthermore, the athlete is still at the risk of early onset of osteoarthritis. Regardless of the gender disparity in the ACL injury rates, a clear understanding of the underlying injury mechanisms is required in order to reduce the incidence of these injuries. Computational modeling is a resourceful and cost effective tool to investigate the biomechanics of the knee. The aim of this study was twofold. The first aim was to develop subject specific computational models of the knee joint and the second aim to gain an improved understanding of the ACL injury mechanisms using the subject specific models. We used a quasi-static, multi-body modeling approach and developed MRI based tibio-femoral computational knee joint models. Experimental joint laxity and combined loading data was obtained using five cadaveric knee specimens and a state-of-the-art robotic system. Ligament zero strain lengths and insertion points were optimized using joint laxity data. Combined loading and ACL strain data were used for model validations. ACL injury simulations were performed using factorial design approach comprising of multiple factors and levels to replicate a large and rich set of loading states. This thesis is an extensive work covering all the details of the ACL injury project explained above and highlighting the importance of 1) computational modeling in inj

Subject Specific Computational Models of the Knee to Predict Anterior Cruciate Ligament Injury

Knee joint is a complex joint involving multiple interactions between cartilage, bone, muscles, ligaments, tendons and neural control. Anterior Cruciate Ligament (ACL) is one ligament in the knee joint that frequently gets injured during various sports or recreational activities. ACL injuries are common in college level and professional athletes especially in females and the injury rate is growing in epidemic proportions despite significant increase in the research focusing on neuromuscular and proprioceptive training programs. Most ACL injuries lead to surgical reconstruction followed by a lengthy rehabilitation program impacting the health and performance of the athlete. Furthermore, the athlete is still at the risk of early onset of osteoarthritis. Regardless of the gender disparity in the ACL injury rates, a clear understanding of the underlying injury mechanisms is required in order to reduce the incidence of these injuries. Computational modeling is a resourceful and cost effective tool to investigate the biomechanics of the knee. The aim of this study was twofold. The first aim was to develop subject specific computational models of the knee joint and the second aim to gain an improved understanding of the ACL injury mechanisms using the subject specific models. We used a quasi-static, multi-body modeling approach and developed MRI based tibio-femoral computational knee joint models. Experimental joint laxity and combined loading data was obtained using five cadaveric knee specimens and a state-of-the-art robotic system. Ligament zero strain lengths and insertion points were optimized using joint laxity data. Combined loading and ACL strain data were used for model validations. ACL injury simulations were performed using factorial design approach comprising of multiple factors and levels to replicate a large and rich set of loading states. This thesis is an extensive work covering all the details of the ACL injury project explained above and highlighting the importance of 1) computational modeling in inj

Validation d'un Nouveau Modèle Statistique de Scapula Augmenté de Marqueurs Anatomiques

International audienceCe papier décrit la validation d'un modèle statistique de scapula (SSM) augmenté d'un ensemble de marqueurs anatomiques ayant un intérêt clinique. Le SSM utilisé est issu de nos récents travaux ayant abouti à la publication d'un des premiers modèles statistiques de l'os scapulaire chez l'humain adulte. En effet, la scapula est une forme 3D difficile à modéliser statistiquement du fait de sa forme complexe et de sa grande variabilité. Ce SSM avait été validé par les critères classiques de robustesse de construction du SSM à savoir, compacité, généralité et spécificité. Cependant, la robustesse de la représentation statistique n'est pas garante de sa validité anatomique pourtant primordiale pour des applications cliniques. Dans cette étude, nous présentons une nouvelle méthode pour l'ajout d'informations anatomiques dans le SSM développé et nous l'évaluons par un processus de sélection des marqueurs anatomiques utilisant un groupe mixte d'observateurs. Nous obtenons d'excellents résultats issus des analyses de variance intra et inter-observateurs. Ces résultats nous permettent d'envisager l'utilisation de ce SSM augmenté pour des applications de segmentation automatique d'IRM et des études biomécaniques du complexe de l'épaule

Subject Specific Computational Models of the Knee to Predict Anterior Cruciate Ligament Injury

Knee joint is a complex joint involving multiple interactions between cartilage, bone, muscles, ligaments, tendons and neural control. Anterior Cruciate Ligament (ACL) is one ligament in the knee joint that frequently gets injured during various sports or recreational activities. ACL injuries are common in college level and professional athletes especially in females and the injury rate is growing in epidemic proportions despite significant increase in the research focusing on neuromuscular and proprioceptive training programs. Most ACL injuries lead to surgical reconstruction followed by a lengthy rehabilitation program impacting the health and performance of the athlete. Furthermore, the athlete is still at the risk of early onset of osteoarthritis. Regardless of the gender disparity in the ACL injury rates, a clear understanding of the underlying injury mechanisms is required in order to reduce the incidence of these injuries. Computational modeling is a resourceful and cost effective tool to investigate the biomechanics of the knee. The aim of this study was twofold. The first aim was to develop subject specific computational models of the knee joint and the second aim to gain an improved understanding of the ACL injury mechanisms using the subject specific models. We used a quasi-static, multi-body modeling approach and developed MRI based tibio-femoral computational knee joint models. Experimental joint laxity and combined loading data was obtained using five cadaveric knee specimens and a state-of-the-art robotic system. Ligament zero strain lengths and insertion points were optimized using joint laxity data. Combined loading and ACL strain data were used for model validations. ACL injury simulations were performed using factorial design approach comprising of multiple factors and levels to replicate a large and rich set of loading states. This thesis is an extensive work covering all the details of the ACL injury project explained above and highlighting the importance of 1) computational modeling in inj

Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network

Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatric imaging data. We propose a new pre-trained regularized convolutional encoder-decoder network for the challenging task of segmenting heterogeneous pediatric magnetic resonance (MR) images. In this direction, we adopt a transfer learning approach along with a regularization strategy to improve the generalization of segmentation models. To this end, we have conceived a novel optimization scheme for the segmentation network which comprises additional regularization terms to the loss function. In order to obtain globally consistent predictions, we incorporate a shape priors based regularization, derived from a non-linear shape representation learnt by an auto-encoder. Additionally, an adversarial regularization computed by a discriminator is integrated to encourage plausible delineations. The proposed method is evaluated for the task of multi-bone segmentation on two scarce pediatric imaging datasets from ankle and shoulder joints, comprising pathological as well as healthy examinations. The proposed method performed either better or at par with previously proposed approaches for Dice, sensitivity, specificity, maximum symmetric surface distance, average symmetric surface distance, and relative absolute volume difference metrics. We illustrate that the proposed approach can be easily integrated into various bone segmentation strategies and can improve the prediction accuracy of models pre-trained on large non-medical images databases. The obtained results bring new perspectives for the management of pediatric musculoskeletal disorders.Comment: 18 pages, 11 figures, 6 table

Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning

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Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network

International audienceMorphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatric imaging data. We propose a new pre-trained regularized convolutional encoder–decoder network for the challenging task of segmenting heterogeneous pediatric magnetic resonance (MR) images. To this end, we have conceived a novel optimization scheme for the segmentation network which comprises additional regularization terms to the loss function. In order to obtain globally consistent predictions, we incorporate a shape priors based regularization, derived from a non-linear shape representation learnt by an auto-encoder. Additionally, an adversarial regularization computed by a discriminator is integrated to encourage precise delineations. The proposed method is evaluated for the task of multi-bone segmentation on two scarce pediatric imaging datasets from ankle and shoulder joints, comprising pathological as well as healthy examinations. The proposed method performed either better or at par with previously proposed approaches for Dice, sensitivity, specificity, maximum symmetric surface distance, average symmetric surface distance, and relative absolute volume difference metrics. We illustrate that the proposed approach can be easily integrated into various bone segmentation strategies and can improve the prediction accuracy of models pre-trained on large non-medical images databases. The obtained results bring new perspectives for the management of pediatric musculoskeletal disorders

An automated statistical shape model developmental pipeline: implications to shoulder surgery parameter

International audienceUsing Statistical Shape Models (SSM) of human scapula (S) and humerus (H) in evaluating surgical parameters can lead to successful outcomes. This work presents an integrated pipeline for building an automated and unbiased global SSM of these bones from a set of CT scans (Sn = 27, Hn = 28). First, an intrinsic consensus shape was established using an Iterative Median Closest Point algorithm (groupwise rigid registration), eliminating the need for manual landmarking/region building that induce bias. Then a mean-virtual (Mv) shape was developed using a Coherence Point Drift method (non-rigid registration). This Mv shape was used to identically resample each of the original datasets with one-to-one correspondences through the basis (Mv estimates). SSM of S and H was derived by conducting a probabilistic Principal Component Analysis on Mv estimates using Statismo toolkit. This method was compared with 1) Expectation Maximization-Iterative Closest Point algorithm, and 2) groupwise Gaussian mixture model based registration on hippocampi data (n = 42) and performed equal to or better than these two methods based on generality, specificity and compactness criteria

Combining shape priors with conditional adversarial networks for improved scapula segmentation in MR images

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