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

Liver segmentation using automatically defined patient specific B-Spline surface models

This paper presents a novel liver segmentation algorithm. This is a model-driven approach; however, unlike previous techniques which use a statistical model obtained from a training set, we initialize patient-specific models directly from their own pre-segmentation. As a result, the non-trivial problems such as landmark correspondences, model registration etc. can be avoided. Moreover, by dividing the liver region into three sub-regions, we convert the problem of building one complex shape model into constructing three much simpler models, which can be fitted independently, greatly improving the computation efficiency. A robust graph-based narrow band optimal surface fitting scheme is also presented. The proposed approach is evaluated on 35 CT images. Compared to contemporary approaches, our approach has no training requirement and requires significantly less processing time, with an RMS error of 2.440.53mm against manual segmentation

An Automatic Level Set Based Liver Segmentation from MRI Data Sets

A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results

Shape parametrization of bio-mechanical finite element models based on medical images

The main objective of this study is to combine the statistical shape analysis with a morphing procedure in order to generate shape-parametric finite element models of tissues and organs and to explore the reliability and the limitations of this approach when applied to databases of real medical images. As classical statistical shape models are not always adapted to the morphing procedure, a new registration method was developed in order to maximize the morphing efficiency. The method was compared to the traditional iterative thin plate spline (iTPS). Two data sets of 33 proximal femora shapes and 385 liver shapes were used for the comparison. The principal component analysis was used to get the principal morphing modes. In terms of anatomical shape reconstruction (evaluated through the criteria of generalization, compactness and specificity), our approach compared fairly well to the iTPS method, while performing remarkably better in terms of mesh quality, since it was less prone to generate invalid meshes in the interior. This was particularly true in the liver case. Such methodology offers a potential application for the generation of automated finite element (FE) models from medical images. Parametrized anatomical models can also be used to assess the influence of inter-patient variability on the biomechanical response of the tissues. Indeed, thanks to the shape parametrization the user would easily have access to a valid FE model for any shape belonging to the parameters subspace