Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities. In this paper, we present an iterative multi-class learning method to segment the femoral, tibial and patellar cartilage simultaneously, which effectively exploits the spatial contextual constraints between bone and cartilage, and also between different cartilages. First, based on the fact that the cartilage grows in only certain area of the corresponding bone surface, we extract the distance features of not only to the surface of the bone, but more informatively, to the densely registered anatomical landmarks on the bone surface. Second, we introduce a set of iterative discriminative classifiers that at each iteration, probability comparison features are constructed from the class confidence maps derived by previously learned classifiers. These features automatically embed the semantic context information between different cartilages of interest. Validated on a total of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the proposed approach demonstrates high robustness and accuracy of segmentation in comparison with existing state-of-the-art MR cartilage segmentation methods.Comment: MICCAI 2013: Workshop on Medical Computer Visio

Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images: data from the Osteoarthritis Initiative

OBJECTIVE: To validate an automatic scheme for the segmentation and quantitative analysis of the medial meniscus (MM) and lateral meniscus (LM) in magnetic resonance (MR) images of the knee

Landmark Localization, Feature Matching and Biomarker Discovery from Magnetic Resonance Images

The work presented in this thesis proposes several methods that can be roughly divided into three different categories: I) landmark localization in medical images, II) feature matching for image registration, and III) biomarker discovery in neuroimaging. The first part deals with the identification of anatomical landmarks. The motivation stems from the fact that the manual identification and labeling of these landmarks is very time consuming and prone to observer errors, especially when large datasets must be analyzed. In this thesis we present three methods to tackle this challenge: A landmark descriptor based on local self-similarities (SS), a subspace building framework based on manifold learning and a sparse coding landmark descriptor based on data-specific learned dictionary basis. The second part of this thesis deals with finding matching features between a pair of images. These matches can be used to perform a registration between them. Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration can be challenging to achieve using intensity based registration algorithms. Here, a framework is proposed for learning correspondences in pairs of images by matching SS features and random sample and consensus (RANSAC) is employed as a robust model estimator to learn a deformation model based on feature matches. Finally, the third part of the thesis deals with biomarker discovery using machine learning. In this section a framework for feature extraction from learned low-dimensional subspaces that represent inter-subject variability is proposed. The manifold subspace is built using data-driven regions of interest (ROI). These regions are learned via sparse regression, with stability selection. Also, probabilistic distribution models for different stages in the disease trajectory are estimated for different class populations in the low-dimensional manifold and used to construct a probabilistic scoring function.Open Acces

CT-based Subchondral Bone Microstructural Analysis in Knee Osteoarthritis via MR-Guided Distillation Learning

Background: MR-based subchondral bone effectively predicts knee osteoarthritis. However, its clinical application is limited by the cost and time of MR. Purpose: We aim to develop a novel distillation-learning-based method named SRRD for subchondral bone microstructural analysis using easily-acquired CT images, which leverages paired MR images to enhance the CT-based analysis model during training. Materials and Methods: Knee joint images of both CT and MR modalities were collected from October 2020 to May 2021. Firstly, we developed a GAN-based generative model to transform MR images into CT images, which was used to establish the anatomical correspondence between the two modalities. Next, we obtained numerous patches of subchondral bone regions of MR images, together with their trabecular parameters (BV / TV, Tb. Th, Tb. Sp, Tb. N) from the corresponding CT image patches via regression. The distillation-learning technique was used to train the regression model and transfer MR structural information to the CT-based model. The regressed trabecular parameters were further used for knee osteoarthritis classification. Results: A total of 80 participants were evaluated. CT-based regression results of trabecular parameters achieved intra-class correlation coefficients (ICCs) of 0.804, 0.773, 0.711, and 0.622 for BV / TV, Tb. Th, Tb. Sp, and Tb. N, respectively. The use of distillation learning significantly improved the performance of the CT-based knee osteoarthritis classification method using the CNN approach, yielding an AUC score of 0.767 (95% CI, 0.681-0.853) instead of 0.658 (95% CI, 0.574-0.742) (p<.001). Conclusions: The proposed SRRD method showed high reliability and validity in MR-CT registration, regression, and knee osteoarthritis classification, indicating the feasibility of subchondral bone microstructural analysis based on CT images.Comment: 5 figures, 4 table

Analysis, Segmentation and Prediction of Knee Cartilage using Statistical Shape Models

Osteoarthritis (OA) of the knee is one of the leading causes of chronic disability (along with the hip). Due to rising healthcare costs associated with OA, it is important to fully understand the disease and how it progresses in the knee. One symptom of knee OA is the degeneration of cartilage in the articulating knee. The cartilage pad plays a major role in painting the biomechanical picture of the knee. This work attempts to quantify the cartilage thickness of healthy male and female knees using statistical shape models (SSMs) for a deep knee bend activity. Additionally, novel cartilage segmentation from magnetic resonance imaging (MRI) and estimation algorithms from computer tomography (CT) or x-rays are proposed to facilitate the efficient development and accurate analysis of future treatments related to the knee. Cartilage morphology results suggest distinct patterns of wear in varus, valgus, and neutral degenerative knees, and examination of contact regions during the deep knee bend activity further emphasizes these patterns. Segmentation results were achieved that were comparable if not of higher quality than existing state-of-the-art techniques for both femoral and tibial cartilage. Likewise, using the point correspondence properties of SSMs, estimation of articulating cartilage was effective in healthy and degenerative knees. In conclusion, this work provides novel, clinically relevant morphological data to compute segmentation and estimate new data in such a way to potentially contribute to improving results and efficiency in evaluation of the femorotibial cartilage layer

Analysis of MRI for Knee Osteoarthritis using Machine Learning

Approximately 8.5 million people in the UK (13.5% of the population) have osteoarthritis (OA) in one or both knees, with more than 6 million people in the UK suffering with painful osteoarthritis of the knee. In addition, an ageing population implies that an estimated 17 million people (twice as many as in 2012) are likely to be living with OA by 2030. Despite this, there exists no disease modifying drugs for OA and structural OA in MRI is poorly characterised. This motivates research to develop biomarkers and tools to aid osteoarthritis diagnosis from MRI of the knee. Previously many solutions for learning biomarkers have relied upon hand-crafted features to characterise and diagnose osteoarthritis from MRI. The methods proposed in this thesis are scalable and use machine learning to characterise large populations of the OAI dataset, with one experiment applying an algorithm to over 10,000 images. Studies of this size enable subtle characteristics of the dataset to be learnt and model many variations within a population. We present data-driven algorithms to learn features to predict OA from the appearance of the articular cartilage. An unsupervised manifold learning algorithm is used to compute a low dimensional representation of knee MR data which we propose as an imaging marker of OA. Previous metrics introduced for OA diagnosis are loosely based on the research communities intuition of the structural causes of OA progression, including morphological measures of the articular cartilage such as the thickness and volume. We demonstrate that there is a strong correlation between traditional morphological measures of the articular cartilage and the biomarkers identified using the manifold learning algorithm that we propose (R 2 = 0.75). The algorithm is extended to create biomarkers for different regions and sequences. A combination of these markers is proposed to yield a diagnostic imaging biomarker with superior performance. The diagnostic biomarkers presented are shown to improve upon hand-crafted morphological measure of disease status presented in the literature, a linear discriminant analysis (LDA) classification for early stage diagnosis of knee osteoarthritis results with an AUC of 0.9. From the biomarker discovery experiments we identified that intensity based affine registration of knee MRIs is not sufficiently robust for large scale image analysis, approximately 5% of these registrations fail. We have developed fast algorithms to compute robust affine transformations of knee MRI, which enables accurate pairwise registrations in large datasets. We model the population of images as a non-linear manifold, a registration is defined by the shortest geodesic path over the manifold representation. We identify sources of error in our manifold representation and propose fast mitigation strategies by checking for consistency across the manifold and by utilising multiple paths. These mitigation strategies are shown to improve registration accuracy and can be computed in less than 2 seconds with current architecture.Open Acces

Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative

Clinical studies including thousands of magnetic resonance imaging (MRI) scans offer potential for pathogenesis research in osteoarthritis. However, comprehensive quantification of all bone, cartilage, and meniscus compartments is challenging. We propose a segmentation framework for fully automatic segmentation of knee MRI. The framework combines multiatlas rigid registration with voxel classification and was trained on manual segmentations with varying configurations of bones, cartilages, and menisci. The validation included high- and low-field knee MRI cohorts from the Center for Clinical and Basic Research, the osteoarthritis initiative (QAI), and the segmentation of knee images10 (SKI10) challenge. In total, 1907 knee MRIs were segmented during the evaluation. No segmentations were excluded. Our resulting OAI cartilage volume scores are available upon request. The precision and accuracy performances matched manual reader re-segmentation well. The cartilage volume scan-rescan precision was 4.9% (RMS CV). The Dice volume overlaps in the medial/lateral tibial/femoral cartilage compartments were 0.80 to 0.87. The correlations with volumes from independent methods were between 0.90 and 0.96 on the OAI scans. Thus, the framework demonstrated precision and accuracy comparable to manual segmentations. Finally, our method placed second for cartilage segmentation in the SKI10 challenge. The comprehensive validation suggested that automatic segmentation is appropriate for cohorts with thousands of scans