Articulated statistical shape models for the analysis of bone destruction in mouse models of rheumatoid arthritis

Rheumatoid arthritis is an autoimmune disease that affects approximately 1% of the population, where chronic inflammation of the synovial joints can lead to active destruction of cartilage and bone. New therapeutic targets are discovered by investigating genes or processes that exacerbate or ameliorate disease progression. Mouse models of inflammatory arthritis are commonly employed for this purpose, in conjunction with biomedical imaging techniques and suitable measures of disease severity. This thesis investigated the hypothesis that a statistical model of non-pathological bone shape variation could be used to quantify bone destruction present in micro-CT images. A framework for constructing statistical shape models of the hind paw was developed, based on articulated registration of a manually segmented reference image. Successful registration of the reference towards ten healthy hind paw samples was followed by statistical shape analysis. Mouse models of inflammatory arthritis were then investigated and compared by identifying bone abnormalities as deviations from the model statistics. Validation of the model against digital phantoms and clinical scores indicates that the method is largely successful in this effort. Application of the method in a novel study of macrophage-mediated inflammation shows promising results that are supportive of previous findings

The role of subchondral bone in osteoarthritis

Osteoarthritis (OA) is the most common form of arthritis. Affected individuals commonly suffer with chronic pain, joint dysfunction, and reduced quality of life. OA also confers an immense burden on health services and economies. Current OA therapies are symptomatic and there are no therapies that modify structural progression. The lack of validated, responsive and reliable biomarkers represents a major barrier to the development of structure-modifying therapies. MRI provides tremendous insight into OA structural disease and has highlighted the importance of subchondral bone in OA. The hypothesis underlying this thesis is that novel quantitative imaging biomarkers of subchondral bone will provide valid measures for OA clinical trials. The Osteoarthritis Initiative (OAI) provided a large natural history database of knee OA to enable testing of the validity of these novel biomarkers. A systematic literature review identified independent associations between subchondral bone features with structural progression, pain and total knee replacement in peripheral joint OA. However very few papers examined the association of 3D bone shape with these patient-centred outcomes. A cross-sectional analysis of the OAI established a significant association between 3D bone area and conventional radiographic OA severity scores, establishing construct validity of 3D bone shape. A nested case-control analysis within the OAI determined that 3D bone shape was associated with the outcome of future total knee replacement, establishing predictive validity for 3D bone shape. A regression analysis within the OAI identified that 3D bone shape was associated with current knee symptoms but not incident symptoms, establishing evidence of concurrent but not predictive validity for new symptoms. In summary, 3D bone shape is an important biomarker of OA which has construct and predictive validity in knee OA. This thesis, along with parallel work on reliability and responsiveness provides evidence supporting its suitability for use in clinical trials

Osteoarthritis: pathogenesis and therapeutic interventions for a whole joint disease

__Abstract__ Osteoarthritis (OA) is an invalidating disease characterized by progressive cartilage degradation. OA is the most prevalent arthritic disease and leading cause of disability that effects approximately 34% of the population in the United states over age 65. Also in the Netherlands, approximately 30% of persons aged 65 and older are affected in either the hip or knee joint by this severely disabling disease. Due to the obvious cartilage pathology, research has much focused on articular cartilage and chondrocyte pathobiology. Over the years more knowledge has been gained on complex biochemical and biomechanical influences of chondrocyte behavior. During the past decade, however, pathologic cellular and structural changes in subchondral and trabecular bone, ligaments, synovium, supporting musculature, fibrocartilagenous structures such as the meniscus, and intra-articular fat tissue support the idea that osteoarthritis is not just a cartilage problem. In the current dogma, OA is explained as ‘a whole joint disease’ that involves a degenerative continuum between multiple joint tissues and cell types

Relationships between image-based and mechanical bone properties with pain in knee osteoarthritis

Pain is the predominant symptom of OA, a debilitating disease marked by changes in cartilage and subchondral bone, but pain pathophysiology is poorly understood. Bone is densely innervated and may be linked to OA-related knee pain. Quantitative computed tomography (QCT) is an in vivo image-based technique with the potential to quantify bone mineral density (BMD) to explore the role of bone in OA-related pain. When coupled with subject-specific finite element (FE) modeling, it may be possible to clarify the mechanical role of bone in OA-related knee pain. The objectives of this study were to assess if: 1) tibial subchondral BMD is associated with OA-related nocturnal knee pain using depth-specific QCT image processing, 2) tibial epiphyseal and metaphyseal BMD is associated with OA-related knee pain using a modified depth-specific CT image processing tool, 3) subchondral cyst characteristics are associated with OA-related knee pain, and 4) FE-derived mechanical outcomes at the proximal tibia are associated with OA-related pain. Lateral focal subchondral BMD was 33% higher in participants with severe nocturnal pain than participants with no nocturnal pain at the 2.5-5mm depth (p=0.028) and 32% higher at 5-10mm from the subchondral surface (p=0.049). At the epiphyseal and metaphyseal depths, higher total pain was associated with lower medial epiphyseal BMD (R2=-0.40, p=0.002), and lower metaphyseal BMD (R2=-0.35, p=0.017). At the lateral region, subchondral cyst number (r=0.55, p<0.001) and cyst number per proximal tibial volume (r=0.52, p<0.001) were both associated with BMD, and lateral cyst number and volume were associated with joint space narrowing (r=0.52 to 0.68, p<0.001) and alignment (r=0.44 to 0.62, p<0.001). In our FE study, principal compressive stress was associated with nocturnal pain at most lateral regions (r=0.33 to 0.50, p<0.05). Principal compressive stress at the lateral region ranged from 47% to 67% higher (p<0.05) in participants with severe nocturnal pain than participants with no pain. This series of studies suggests that pain in patients with knee OA may be associated with BMD throughout various depths at the proximal tibia as well as FE-based bone mechanical outcomes, such as principal compressive stress. These findings suggest previously unexplored associations between OA-related knee pain and BMD or mechanical outcomes, emphasizing that bone may have a mechanical role in OA-related pain pathogenesis

Outlier Detection for Shape Model Fitting

Medical image analysis applications often benefit from having a statistical shape model in the background. Statistical shape models are generative models which can generate shapes from the same family and assign a likelihood to the generated shape. In an Analysis-by-synthesis approach to medical image analysis, the target shape to be segmented, registered or completed must first be reconstructed by the statistical shape model. Shape models accomplish this by either acting as regression models, used to obtain the reconstruction, or as regularizers, used to limit the space of possible reconstructions. However, the accuracy of these models is not guaranteed for targets that lie out of the modeled distribution of the statistical shape model. Targets with pathologies are an example of out-of-distribution data. The target shape to be reconstructed has deformations caused by pathologies that do not exist on the healthy data used to build the model. Added and missing regions may lead to false correspondences, which act as outliers and influence the reconstruction result. Robust fitting is necessary to decrease the influence of outliers on the fitting solution, but often comes at the cost of decreased accuracy in the inlier region. Robust techniques often presuppose knowledge of outlier characteristics to build a robust cost function or knowledge of the correct regressed function to filter the outliers. This thesis proposes strategies to obtain the outliers and reconstruction simultaneously without previous knowledge about either. The assumptions are that a statistical shape model that represents the healthy variations of the target organ is available, and that some landmarks on the model reference that annotate locations with correspondence to the target exist. The first strategy uses an EM-like algorithm to obtain the sampling posterior. This is a global reconstruction approach that requires classical noise assumptions on the outlier distribution. The second strategy uses Bayesian optimization to infer the closed-form predictive posterior distribution and estimate a label map of the outliers. The underlying regression model is a Gaussian Process Morphable Model (GPMM). To make the reconstruction obtained through Bayesian optimization robust, a novel acquisition function is proposed. The acquisition function uses the posterior and predictive posterior distributions to avoid choosing outliers as next query points. The algorithms give as outputs a label map and a a posterior distribution that can be used to choose the most likely reconstruction. To obtain the label map, the first strategy uses Bayesian classification to separate inliers and outliers, while the second strategy annotates all query points as inliers and unused model vertices as outliers. The proposed solutions are compared to the literature, evaluated through their sensitivity and breakdown points, and tested on publicly available datasets and in-house clinical examples. The thesis contributes to shape model fitting to pathological targets by showing that: - performing accurate inlier reconstruction and outlier detection is possible without case-specific manual thresholds or input label maps, through the use of outlier detection. - outlier detection makes the algorithms agnostic to pathology type i.e. the algorithms are suitable for both sparse and grouped outliers which appear as holes and bumps, the severity of which influences the results. - using the GPMM-based sequential Bayesian optimization approach, the closed-form predictive posterior distribution can be obtained despite the presence of outliers, because the Gaussian noise assumption is valid for the query points. - using sequential Bayesian optimization instead of traditional optimization for shape model fitting brings forth several advantages that had not been previously explored. Fitting can be driven by different reconstruction goals such as speed, location-dependent accuracy, or robustness. - defining pathologies as outliers opens the door for general pathology segmentation solutions for medical data. Segmentation algorithms do not need to be dependent on imaging modality, target pathology type, or training datasets for pathology labeling. The thesis highlights the importance of outlier-based definitions of pathologies in medical data that are independent of pathology type and imaging modality. Developing such standards would not only simplify the comparison of different pathology segmentation algorithms on unlabeled datsets, but also push forward standard algorithms that are able to deal with general pathologies instead of data-driven definitions of pathologies. This comes with theoretical as well as clinical advantages. Practical applications are shown on shape reconstruction and labeling tasks. Publicly-available challenge datasets are used, one for cranium implant reconstruction, one for kidney tumor detection, and one for liver shape reconstruction. Further clinical applications are shown on in-house examples of a femur and mandible with artifacts and missing parts. The results focus on shape modeling but can be extended in future work to include intensity information and inner volume pathologies

Probabilistic and geometric shape based segmentation methods.

Image segmentation is one of the most important problems in image processing, object recognition, computer vision, medical imaging, etc. In general, the objective of the segmentation is to partition the image into the meaningful areas using the existing (low level) information in the image and prior (high level) information which can be obtained using a number of features of an object. As stated in [1,2], the human vision system aims to extract and use as much information as possible in the image including but not limited to the intensity, possible motion of the object (in sequential images), spatial relations (interaction) as the existing information, and the shape of the object which is learnt from the experience as the prior information. The main objective of this dissertation is to couple the prior information with the existing information since the machine vision system cannot predict the prior information unless it is given. To label the image into meaningful areas, the chosen information is modelled to fit progressively in each of the regions by an optimization process. The intensity and spatial interaction (as the existing information) and shape (as the prior information) are modeled to obtain the optimum segmentation in this study. The intensity information is modelled using the Gaussian distribution. Spatial interaction that describes the relation between neighboring pixels/voxels is modelled by assuming that the pixel intensity depends on the intensities of the neighboring pixels. The shape model is obtained using occurrences of histogram of training shape pixels or voxels. The main objective is to capture the shape variation of the object of interest. Each pixel in the image will have three probabilities to be an object and a background class based on the intensity, spatial interaction, and shape models. These probabilistic values will guide the energy (cost) functionals in the optimization process. This dissertation proposes segmentation frameworks which has the following properties: i) original to solve some of the existing problems, ii) robust under various segmentation challenges, and iii) fast enough to be used in the real applications. In this dissertation, the models are integrated into different methods to obtain the optimum segmentation: 1) variational (can be considered as the spatially continuous), and 2) statistical (can be considered as the spatially discrete) methods. The proposed segmentation frameworks start with obtaining the initial segmentation using the intensity / spatial interaction models. The shape model, which is obtained using the training shapes, is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of the energy functionals. Experiments show that the use of the shape prior improves considerably the accuracy of the alternative methods which use only existing or both information in the image. The proposed methods are tested on the synthetic and clinical images/shapes and they are shown to be robust under various noise levels, occlusions, and missing object information. Vertebral bodies (VBs) in clinical computed tomography (CT) are segmented using the proposed methods to help the bone mineral density measurements and fracture analysis in bones. Experimental results show that the proposed solutions eliminate some of the existing problems in the VB segmentation. One of the most important contributions of this study is to offer a segmentation framework which can be suitable to the clinical works