Automatic generation of statistical pose and shape models for articulated joints

Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity

An Efficient and Robust Algorithm for Parallel Groupwise Registration of Bone Surfaces

Abstract. In this paper a novel groupwise registration algorithm is proposed for the unbiased registration of a large number of densely sampled point clouds. The method fits an evolving mean shape to each of the example point clouds thereby minimizing the total deformation. The registration algorithm alternates between a computationally expensive, but parallelizable, deformation step of the mean shape to each example shape and a very inexpensive step updating the mean shape. The algorithm is evaluated by comparing it to a state of the art registration algorith

Towards a framework for multi class statistical modelling of shape, intensity, and kinematics in medical images

Statistical modelling has become a ubiquitous tool for analysing of morphological variation of bone structures in medical images. For radiological images, the shape, relative pose between the bone structures and the intensity distribution are key features often modelled separately. A wide range of research has reported methods that incorporate these features as priors for machine learning purposes. Statistical shape, appearance (intensity profile in images) and pose models are popular priors to explain variability across a sample population of rigid structures. However, a principled and robust way to combine shape, pose and intensity features has been elusive for four main reasons: 1) heterogeneity of the data (data with linear and non-linear natural variation across features); 2) sub-optimal representation of three-dimensional Euclidean motion; 3) artificial discretization of the models; and 4) lack of an efficient transfer learning process to project observations into the latent space. This work proposes a novel statistical modelling framework for multiple bone structures. The framework provides a latent space embedding shape, pose and intensity in a continuous domain allowing for new approaches to skeletal joint analysis from medical images. First, a robust registration method for multi-volumetric shapes is described. Both sampling and parametric based registration algorithms are proposed, which allow the establishment of dense correspondence across volumetric shapes (such as tetrahedral meshes) while preserving the spatial relationship between them. Next, the framework for developing statistical shape-kinematics models from in-correspondence multi-volumetric shapes embedding image intensity distribution, is presented. The framework incorporates principal geodesic analysis and a non-linear metric for modelling the spatial orientation of the structures. More importantly, as all the features are in a joint statistical space and in a continuous domain; this permits on-demand marginalisation to a region or feature of interest without training separate models. Thereafter, an automated prediction of the structures in images is facilitated by a model-fitting method leveraging the models as priors in a Markov chain Monte Carlo approach. The framework is validated using controlled experimental data and the results demonstrate superior performance in comparison with state-of-the-art methods. Finally, the application of the framework for analysing computed tomography images is presented. The analyses include estimation of shape, kinematic and intensity profiles of bone structures in the shoulder and hip joints. For both these datasets, the framework is demonstrated for segmentation, registration and reconstruction, including the recovery of patient-specific intensity profile. The presented framework realises a new paradigm in modelling multi-object shape structures, allowing for probabilistic modelling of not only shape, but also relative pose and intensity as well as the correlations that exist between them. Future work will aim to optimise the framework for clinical use in medical image analysis

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.Comment: Paper under revie

A computational framework for canonical holistic morphometric analysis of trabecular bone

Bone is a remarkable, living tissue that functionally adapts to external loading. Therefore, bone shape and internal structure carry information relevant to many disciplines, including medicine, forensic science, and anthropology. However, morphometric comparisons of homologous regions across different individuals or groups are still challenging. In this study, two methods were combined to quantify such differences: (1) Holistic morphometric analysis (HMA) was used to quantify morphometric values in each bone, (2) which could then be mapped to a volumetric mesh of a canonical bone created by a statistical free-form deformation model (SDM). Required parameters for this canonical holistic morphometric analysis (cHMA) method were identified and the robustness of the method was evaluated. The robustness studies showed that the SDM converged after one to two iterations, had only a marginal bias towards the chosen starting image, and could handle large shape differences seen in bones of different species. Case studies were performed on metacarpal bones and proximal femora of different primate species to confirm prior study results. The differences between species could be visualised and statistically analysed in both case studies. cHMA provides a framework for performing quantitative comparisons of different morphometric quantities across individuals or groups. These comparisons facilitate investigation of the relationship between spatial morphometric variations and function or pathology, or both

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

Käden konfiguraatioiden estimointi viittomakielisistä videoista

A computer vision system is presented that can locate and classify the handshape from an individual sign-language video frame, using a synthetic 3D model. The system requires no training data; only phonetically-motivated descriptions of sign-language hand configuration classes are required. Experiments were conducted with realistically low-quality sign-language video dictionary footage to test various features and metrics to fix the camera parameters of a fixed synthetic hand model to find the best match of the model to the input frame. Histogram of Oriented Gradients (HOG) features with Euclidean distance turned out to be suitable for this purpose. A novel approach, called Trimmed HOGs, with Earth Mover's Distance, as well as simplistic contours and Canny edges with the chamfer distance, also performed favorably. Minimizing the cost function built from these measures with gradient descent optimization further improved the camera parameter fitting results. Classification of images of handshapes into hand configuration classes with nearest-neighbor classifiers built around the chamfer distance between contours and Canny edges, and chi^2 distance between Pyramidal HOG descriptors turned out to yield reasonable accuracy. Although the system displayed only moderate success rates in a full 26-class scenario, the system was able to reach nearly perfect discriminatory accuracy in a binary classification case, and up to 40 % accuracy when images from a restricted set of 12 classes were classified into six hand configuration groups. Considering that the footage used to evaluate the system was of very poor quality, with future improvements, the methods evaluated may be used as basis for a practical system for automatic annotation of sign language video corpora.Työssä esitetään tietokonenäköjärjestelmä, joka pystyy löytämään ja luokittelemaan käsimuotoja yksittäisistä viittomakielisten videoiden ruuduista synteettistä 3D-mallia käyttäen. Järjestelmä ei vaadi opetusdataa; pelkät foneettisesti motivoidut kuvaukset käden konfiguraatioluokista riittävät. Kokeissa testattiin erilaisia piirteitä ja metriikoita staattisen käsimallin kameraparametrien kiinnittämiseksi, jotta löydettäisiin paras vastaavuus mallin ja syötekuvan välillä. Kokeet ajettiin realistisen heikkolaatuisella videoaineistolla. Gradienttihistogrammit euklidisella etäisyydellä osoittautuivat sopiviksi tähän tarkoitukseen. Uusi työssä esitetty lähestymistapa, jota kutsutaan trimmatuksi gradienttihistogrammiksi, maansiirtäjän etäisyyden (Earth Mover's Distance) kanssa toimi myös hyvin, kuten myös yksinkertaiset ääriviivat ja Canny-reunat chamfer-etäisyyden kanssa. Gradienttilaskeumaoptimointi (gradient descent optimization) paransi kameraparametrien sovitustuloksia. Syötekuvia luokiteltiin lähimmän naapurin luokittimilla, ja ääriviiva- ja Canny-reunapiirteiden chamfer-etäisyyteen sekä pyramidisten gradienttihistogrammien chi^2-etäisyyteen pohjautuvat luokittimet osoittautuivat toimiviksi. Vaikka järjestelmän luokittelutarkkuus jäi vaatimattomaksi täydessä 26 luokan tapauksessa, järjestelmä saavutti liki täydellisen luokittelutarkkuuden binääriluokittelutapauksessa, ja saavutti jopa 40 % tarkkuuden, kun 12 luokan osajoukosta poimittuja kuvia luokiteltiin kuuteen eri ryhmään. Ottaen huomioon aineiston heikosta laadusta johtuvan vaativuuden, voidaan pitää uskottavana, että esitettyjä menetelmiä voidaan käyttää käytännöllisen korpusaineiston automaattiseen annotointiin soveltuvan järjestelmän pohjana

Advanced Imaging of Inflammation in Knee Osteoarthritis

This thesis focuses on imaging methods to study the role of inflammation in knee osteoarthritis. The aims of this thesis are I) to evaluate disturbed perfusion patterns in subchondral bone and the infrapatellar fat pad using perfusion MRI, and II) to assess new magnetic resonance and ultrasound imaging methods for diagnosis of synovitis in knee osteoarthritis