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

Medical Image Analysis: Progress over two decades and the challenges ahead

International audienceThe analysis of medical images has been woven into the fabric of the pattern analysis and machine intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come

Doctor of Philosophy

dissertationThe statistical study of anatomy is one of the primary focuses of medical image analysis. It is well-established that the appropriate mathematical settings for such analyses are Riemannian manifolds and Lie group actions. Statistically defined atlases, in which a mean anatomical image is computed from a collection of static three-dimensional (3D) scans, have become commonplace. Within the past few decades, these efforts, which constitute the field of computational anatomy, have seen great success in enabling quantitative analysis. However, most of the analysis within computational anatomy has focused on collections of static images in population studies. The recent emergence of large-scale longitudinal imaging studies and four-dimensional (4D) imaging technology presents new opportunities for studying dynamic anatomical processes such as motion, growth, and degeneration. In order to make use of this new data, it is imperative that computational anatomy be extended with methods for the statistical analysis of longitudinal and dynamic medical imaging. In this dissertation, the deformable template framework is used for the development of 4D statistical shape analysis, with applications in motion analysis for individualized medicine and the study of growth and disease progression. A new method for estimating organ motion directly from raw imaging data is introduced and tested extensively. Polynomial regression, the staple of curve regression in Euclidean spaces, is extended to the setting of Riemannian manifolds. This polynomial regression framework enables rigorous statistical analysis of longitudinal imaging data. Finally, a new diffeomorphic model of irrotational shape change is presented. This new model presents striking practical advantages over standard diffeomorphic methods, while the study of this new space promises to illuminate aspects of the structure of the diffeomorphism group

Combining local features and region segmentation: methods and applications

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 23-01-2020Esta tesis tiene embargado el acceso al texto completo hasta el 23-07-2021Muchas y muy diferentes son las propuestas que se han desarrollado en el área de la visión artificial para la extracción de información de las imágenes y su posterior uso. Entra las más destacadas se encuentran las conocidas como características locales, del inglés local features, que detectan puntos o áreas de la imagen con ciertas características de interés, y las describen usando información de su entorno (local). También destacan las regiones en este área, y en especial este trabajo se ha centrado en los segmentadores en regiones, cuyo objetivo es agrupar la información de la imagen atendiendo a diversos criterios. Pese al enorme potencial de estas técnicas, y su probado éxito en diversas aplicaciones, su definición lleva implícita una serie de limitaciones funcionales que les han impedido exportar sus capacidades a otras áreas de aplicación. Se pretende impulsar el uso de estas herramientas en dichas aplicaciones, y por tanto mejorar los resultados del estado del arte, mediante la propuesta de un marco de desarrollo de nuevas soluciones. En concreto, la hipótesis principal del proyecto es que las capacidades de las características locales y los segmentadores en regiones son complementarias, y que su combinación, realizada de la forma adecuada, las maximiza a la vez que minimiza sus limitaciones. El principal objetivo, y por tanto la principal contribución del proyecto, es validar dicha hipótesis mediante la propuesta de un marco de desarrollo de nuevas soluciones combinando características locales y segmentadores para técnicas con capacidades mejoradas. Al tratarse de un marco de combinación de dos técnicas, el proceso de validación se ha llevado a cabo en dos pasos. En primer lugar se ha planteado el caso del uso de segmentadores en regiones para mejorar las características locales. Para verificar la viabilidad y el éxito de esta combinación se ha desarrollado una propuesta específica, SP-SIFT, que se ha validado tanto a nivel experimental como a nivel de aplicación real, en concreto como técnica principal de algoritmos de seguimiento de objetos. En segundo lugar, se ha planteado el caso de uso de características locales para mejorar los segmentadores en regiones. Para verificar la viabilidad y el éxito de esta combinación se ha desarrollado una propuesta específica, LF-SLIC, que se ha validado tanto a nivel experimental como a nivel de aplicación real, en concreto como técnica principal de un algoritmo de segmentación de lesiones pigmentadas de la piel. Los resultados conceptuales han probado que las técnicas mejoran a nivel de capacidades. Los resultados aplicados han probado que estas mejoras permiten el uso de estas técnicas en aplicaciones donde antes no tenían éxito. Con ello, se ha considerado la hipótesis validada, y por tanto exitosa la definición de un marco para el desarrollo de nuevas técnicas específicas con capacidades mejoradas. En conclusión, la principal aportación de la tesis es el marco de combinación de técnicas, plasmada en sus dos propuestas específicas: características locales mejoradas con segmentadores y segmentadores mejorados con características locales, y en el éxito conseguido en sus aplicaciones.A huge number of proposals have been developed in the area of computer vision for information extraction from images, and its further use. One of the most prevalent solutions are those known as local features. They detect points or areas of the image with certain characteristics of interest, and describe them using information from their (local) environment. The regions also stand out in the area, and especially this work has focused on the region segmentation algorithms, whose objective is to group the information of the image according to di erent criteria. Despite the enormous potential of these techniques, and their proven success in a number of applications, their de nition implies a series of functional limitations that have prevented them from exporting their capabilities to other application areas. In this thesis, it is intended to promote the use of these tools in these applications, and therefore improve the results of the state of the art, by proposing a framework for developing new solutions. Speci cally, the main hypothesis of the project is that the capacities of the local features and the region segmentation algorithms are complementary, and thus their combination, carried out in the right way, maximizes them while minimizing their limitations. The main objective, and therefore the main contribution of the thesis, is to validate this hypothesis by proposing a framework for developing new solutions combining local features and region segmentation algorithms, obtaining solutions with improved capabilities. As the hypothesis is proposing to combine two techniques, the validation process has been carried out in two steps. First, the use case of region segmentation algorithms enhancing local features. In order to verify the viability and success of this combination, a speci c proposal, SP-SIFT, was been developed. This proposal was validated both experimentally and in a real application scenario, speci cally as the main technique of object tracking algorithms. Second, the use case of enhancing region segmentation algorithm with local features. In order to verify the viability and success of this combination, a speci c proposal, LF-SLIC, was developed. The proposal was validated both experimentally and in a real application scenario, speci cally as the main technique of a pigmented skin lesions segmentation algorithm. The conceptual results proved that the techniques improve at the capabilities level. The application results proved that these improvements allow the use of this techniques in applications where they were previously unsuccessful. Thus, the hypothesis can be considered validated, and therefore the de nition of a framework for the development of new techniques with improved capabilities can be considered successful. In conclusion, the main contribution of the thesis is the framework for the combination of techniques, embodied in the two speci c proposals: enhanced local features with region segmentation algorithms, and region segmentation algorithms enhanced with local features; and in the success achieved in their applications.The work described in this Thesis was carried out within the Video Processing and Understanding Lab at the Department of Tecnología Electrónica y de las Comunicaciones, Escuela Politécnica Superior, Universidad Autónoma de Madrid (from 2014 to 2019). It was partially supported by the Spanish Government (TEC2014-53176-R, HAVideo)

Accurate determination and application of local strain for studying tissues with gradients in mechanical properties

Determination of the mechanical behavior of materials requires an understanding of deformation during loading. While this is traditionally accomplished in engineering by examining a force displacement curve for a whole sample, these techniques implicitly ignore local geometric complexities and local material inhomogeneities commonly found in biologic tissues. Techniques such as normalized cross correlation have been classically applied to address this issue and resolve deformation at the local level; however, these techniques have proven unreliable when deformations become large, if the sample undergoes a rotation, and/or if strain fields become incompatible (e.g. at or near failure). Presented here is a toolbox of techniques that addresses the limitations of the prior state-of-the-art for localized strain estimation. The first algorithm, termed 2D direct deformation estimation (2D-DDE), directly incorporates concepts from mechanics into non-rigid registration algorithms from computer vision, eliminating the need to consider displacement fields, as required for all of the prior state-of-the-art techniques. This results in not only an improvement in accuracy and precision of deformation estimation, but also relaxes compatibility of the deformation fields. A second algorithm, 2D Strain Inference with Measures of Probable Local Elevation (2D-SIMPLE), incorporates the results of 2D-DDE with results from algorithms that enforce strain compatibility to develop a robust detector of strain concentrations. While tracking local strain in a vinylidene chloride sheet in tension, 2D-SIMPLE detected strain concentrations which predicted the initiation of a crack in the material and the progression of the crack tip. The third and fourth algorithms generalize the two dimensional algorithms to analyze three dimensional deformations in volumetric images (3D-DDE and 3D-SIMPLE, respectively). Lastly, the 2D-DDE algorithm is modified to estimate two dimensional surface deformation from multi-view imaging systems. The robustness and adaptability of these techniques was then validated and demonstrated on a wide variety of biomedical applications. Using 2D-DDE, a microscale compliant region was discovered at the tendon-to-bone attachment, local heterogeneity of partially mineralized scaffolds was revealed, and gradients in stiffness of partially mineralized nano-fiber scaffolds were demonstrated. Using 2D-SIMPLE, mechanisms of embryonic wound healing and associated strain localizations were elucidated. 3D-DDE confirmed the existence of strain gradients across chordae tendineae in beating murine hearts as well as demonstrated dramatic localized changes in wall deformation before and after myocardial infarction in murine hearts. 2D-DDE was also used to develop a model system to study the effects of applied stress versus the effects of applied strain on cells. The model system was first theorized by considering a system in which gradients of cross sectional area or scaffold shape were composed with gradients in material stiffness. By combining these gradients in novel ways, it was theoretically determined that stress and strain could be locally isolated. A tensile bioreactor was constructed, techniques for fabricating scaffolds with gradients in stiffness and gradients in cross sectional area were developed, and theoretical strain gradients were confirmed experimentally using 2D-DDE. The model system was then validated for in vitro cell studies. Cell adhesion, proliferation, and viability following a seven day loading protocol were explored. Methods for determining single cell responses, which could be correlated back to a specific stress or strain states, were developed using immunocytochemistry and 2D-DDE approaches. Future studies will apply this model system to determine precise mechanotransduction responses of cells. These studies are critical to optimize stem cell tissue engineering strategies as well inform cell mechanobiology mechanisms

Proceedings of the First International Workshop on Mathematical Foundations of Computational Anatomy (MFCA'06) - Geometrical and Statistical Methods for Modelling Biological Shape Variability

International audienceNon-linear registration and shape analysis are well developed research topic in the medical image analysis community. There is nowadays a growing number of methods that can faithfully deal with the underlying biomechanical behaviour of intra-subject shape deformations. However, it is more difficult to relate the anatomical shape of different subjects. The goal of computational anatomy is to analyse and to statistically model this specific type of geometrical information. In the absence of any justified physical model, a natural attitude is to explore very general mathematical methods, for instance diffeomorphisms. However, working with such infinite dimensional space raises some deep computational and mathematical problems. In particular, one of the key problem is to do statistics. Likewise, modelling the variability of surfaces leads to rely on shape spaces that are much more complex than for curves. To cope with these, different methodological and computational frameworks have been proposed. The goal of the workshop was to foster interactions between researchers investigating the combination of geometry and statistics for modelling biological shape variability from image and surfaces. A special emphasis was put on theoretical developments, applications and results being welcomed as illustrations. Contributions were solicited in the following areas: * Riemannian and group theoretical methods on non-linear transformation spaces * Advanced statistics on deformations and shapes * Metrics for computational anatomy * Geometry and statistics of surfaces 26 submissions of very high quality were recieved and were reviewed by two members of the programm committee. 12 papers were finally selected for oral presentations and 8 for poster presentations. 16 of these papers are published in these proceedings, and 4 papers are published in the proceedings of MICCAI'06 (for copyright reasons, only extended abstracts are provided here)

Estimation of probability distribution on multiple anatomical objects and evaluation of statistical shape models

The estimation of shape probability distributions of anatomic structures is a major research area in medical image analysis. The statistical shape descriptions estimated from training samples provide means and the geometric shape variations of such structures. These are key components in many applications. This dissertation presents two approaches to the estimation of a shape probability distribution of a multi-object complex. Both approaches are applied to objects in the male pelvis, and show improvement in the estimated shape distributions of the objects. The first approach is to estimate the shape variation of each object in the complex in terms of two components: the object's variation independent of the effect of its neighboring objects; and the neighbors' effect on the object. The neighbors' effect on the target object is interpreted using the idea on which linear mixed models are based. The second approach is to estimate a conditional shape probability distribution of a target object given its neighboring objects. The estimation of the conditional probability is based on principal component regression. This dissertation also presents a measure to evaluate the estimated shape probability distribution regarding its predictive power, that is, the ability of a statistical shape model to describe unseen members of the population. This aspect of statistical shape models is of key importance to any application that uses shape models. The measure can be applied to PCA-based shape models and can be interpreted as a ratio of the variation of new data explained by the retained principal directions estimated from training data. This measure was applied to shape models of synthetic warped ellipsoids and right hippocampi. According to two surface distance measures and a volume overlap measure it was empirically verified that the predictive measure reflects what happens in the ambient space where the model lies