A GPU framework for parallel segmentation of volumetric images using discrete deformable models

Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the syste

Active contours with weighted external forces for medical image segmentation

Parametric active contours have been widely used for image segmentation. However, high noise levels and weak edges are the most acute issues that hinder their performance, particularly in medical images. In order to overcome these issues, we propose an external force that weights the gradient vector flow (GVF) field and balloon forces according to local image features. We also propose a mechanism to automatically terminate the contour's deformation. % process. %Our approach improves performance over noisy images and weak edges and allows snake's initialization using a limited number of manually selected points. Evaluation results on real MRI and CT slices show that the proposed approach attains higher segmentation accuracy than snakes using traditional external forces, while allowing initialization using a limited number of selected points

State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities

Segmentation of medical images is an important step in various applications such as visualization, quantitative analysis and image-guided surgery. Numerous segmentation methods have been developed in the past two decades for extraction of organ contours on medical images. Low-level segmentation methods, such as pixel-based clustering, region growing, and filter-based edge detection, require additional pre-processing and post-processing as well as considerable amounts of expert intervention or information of the objects of interest. Furthermore the subsequent analysis of segmented objects is hampered by the primitive, pixel or voxel level representations from those region-based segmentation. Deformable models, on the other hand, provide an explicit representation of the boundary and the shape of the object. They combine several desirable features such as inherent connectivity and smoothness, which counteract noise and boundary irregularities, as well as the ability to incorporate knowledge about the object of interest. However, parametric deformable models have two main limitations. First, in situations where the initial model and desired object boundary differ greatly in size and shape, the model must be re-parameterized dynamically to faithfully recover the object boundary. The second limitation is that it has difficulty dealing with topological adaptation such as splitting or merging model parts, a useful property for recovering either multiple objects or objects with unknown topology. This difficulty is caused by the fact that a new parameterization must be constructed whenever topology change occurs, which requires sophisticated schemes. Level set deformable models, also referred to as geometric deformable models, provide an elegant solution to address the primary limitations of parametric deformable models. These methods have drawn a great deal of attention since their introduction in 1988. Advantages of the contour implicit formulation of the deformable model over parametric formulation include: (1) no parameterization of the contour, (2) topological flexibility, (3) good numerical stability, (4) straightforward extension of the 2D formulation to n-D. Recent reviews on the subject include papers from Suri. In this chapter we give a general overview of the level set segmentation methods with emphasize on new frameworks recently introduced in the context of medical imaging problems. We then introduce novel approaches that aim at combining segmentation and registration in a level set formulation. Finally we review a selective set of clinical works with detailed validation of the level set methods for several clinical applications

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

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

Novel Approaches to the Representation and Analysis of 3D Segmented Anatomical Districts

Nowadays, image processing and 3D shape analysis are an integral part of clinical practice and have the potentiality to support clinicians with advanced analysis and visualization techniques. Both approaches provide visual and quantitative information to medical practitioners, even if from different points of view. Indeed, shape analysis is aimed at studying the morphology of anatomical structures, while image processing is focused more on the tissue or functional information provided by the pixels/voxels intensities levels. Despite the progress obtained by research in both fields, a junction between these two complementary worlds is missing. When working with 3D models analyzing shape features, the information of the volume surrounding the structure is lost, since a segmentation process is needed to obtain the 3D shape model; however, the 3D nature of the anatomical structure is represented explicitly. With volume images, instead, the tissue information related to the imaged volume is the core of the analysis, while the shape and morphology of the structure are just implicitly represented, thus not clear enough. The aim of this Thesis work is the integration of these two approaches in order to increase the amount of information available for physicians, allowing a more accurate analysis of each patient. An augmented visualization tool able to provide information on both the anatomical structure shape and the surrounding volume through a hybrid representation, could reduce the gap between the two approaches and provide a more complete anatomical rendering of the subject. To this end, given a segmented anatomical district, we propose a novel mapping of volumetric data onto the segmented surface. The grey-levels of the image voxels are mapped through a volume-surface correspondence map, which defines a grey-level texture on the segmented surface. The resulting texture mapping is coherent to the local morphology of the segmented anatomical structure and provides an enhanced visual representation of the anatomical district. The integration of volume-based and surface-based information in a unique 3D representation also supports the identification and characterization of morphological landmarks and pathology evaluations. The main research contributions of the Ph.D. activities and Thesis are: \u2022 the development of a novel integration algorithm that combines surface-based (segmented 3D anatomical structure meshes) and volume-based (MRI volumes) information. The integration supports different criteria for the grey-levels mapping onto the segmented surface; \u2022 the development of methodological approaches for using the grey-levels mapping together with morphological analysis. The final goal is to solve problems in real clinical tasks, such as the identification of (patient-specific) ligament insertion sites on bones from segmented MR images, the characterization of the local morphology of bones/tissues, the early diagnosis, classification, and monitoring of muscle-skeletal pathologies; \u2022 the analysis of segmentation procedures, with a focus on the tissue classification process, in order to reduce operator dependency and to overcome the absence of a real gold standard for the evaluation of automatic segmentations; \u2022 the evaluation and comparison of (unsupervised) segmentation methods, finalized to define a novel segmentation method for low-field MR images, and for the local correction/improvement of a given segmentation. The proposed method is simple but effectively integrates information derived from medical image analysis and 3D shape analysis. Moreover, the algorithm is general enough to be applied to different anatomical districts independently of the segmentation method, imaging techniques (such as CT), or image resolution. The volume information can be integrated easily in different shape analysis applications, taking into consideration not only the morphology of the input shape but also the real context in which it is inserted, to solve clinical tasks. The results obtained by this combined analysis have been evaluated through statistical analysis

Region tracking on surfaces deforming via level-sets methods

PostprintSince the work by Osher and Sethian (1988) on level-sets algorithms for numerical shape evolutions, this technique has been used for a large number of applications in numerous fields. In medical imaging, this numerical technique has been successfully used, for example, in segmentation and cortex unfolding algorithms. The migration from a Lagrangian implementation to a Eulerian one via implicit representations or level-sets brought some of the main advantages of the technique, i.e., topology independence and stability. This migration means also that the evolution is parametrization free. Therefore, the authors do not know exactly how each part of the shape is deforming and the point-wise correspondence is lost. In this note they present a technique to numerically track regions on surfaces that are being deformed using the level-sets method. The basic idea is to represent the region of interest as the intersection of two implicit surfaces and then track its deformation from the deformation of these surfaces. This technique then solves one of the main shortcomings of the very useful level-sets approach. Applications include lesion localization in medical images, region tracking in functional MRI (fMRI) visualization, and geometric surface mapping