Automatic Affine and Elastic Registration Strategies for Multi-dimensional Medical Images

Medical images have been used increasingly for diagnosis, treatment planning, monitoring disease processes, and other medical applications. A large variety of medical imaging modalities exists including CT, X-ray, MRI, Ultrasound, etc. Frequently a group of images need to be compared to one another and/or combined for research or cumulative purposes. In many medical studies, multiple images are acquired from subjects at different times or with different imaging modalities. Misalignment inevitably occurs, causing anatomical and/or functional feature shifts within the images. Computerized image registration (alignment) approaches can offer automatic and accurate image alignments without extensive user involvement and provide tools for visualizing combined images. This dissertation focuses on providing automatic image registration strategies. After a through review of existing image registration techniques, we identified two registration strategies that enhance the current field: (1) an automated rigid body and affine registration using voxel similarity measurements based on a sequential hybrid genetic algorithm, and (2) an automated deformable registration approach based upon a linear elastic finite element formulation. Both methods streamlined the registration process. They are completely automatic and require no user intervention. The proposed registration strategies were evaluated with numerous 2D and 3D MR images with a variety of tissue structures, orientations and dimensions. Multiple registration pathways were provided with guidelines for their applications. The sequential genetic algorithm mimics the pathway of an expert manually doing registration. Experiments demonstrated that the sequential genetic algorithm registration provides high alignment accuracy and is reliable for brain tissues. It avoids local minima/maxima traps of conventional optimization techniques, and does not require any preprocessing such as threshold, smoothing, segmentation, or definition of base points or edges. The elastic model was shown to be highly effective to accurately align areas of interest that are automatically extracted from the images, such as brains. Using a finite element method to get the displacement of each element node by applying a boundary mapping, this method provides an accurate image registration with excellent boundary alignment of each pair of slices and consequently align the entire volume automatically. This dissertation presented numerous volume alignments. Surface geometries were created directly from the aligned segmented images using the Multiple Material Marching Cubes algorithm. Using the proposed registration strategies, multiple subjects were aligned to a standard MRI reference, which is aligned to a segmented reference atlas. Consequently, multiple subjects are aligned to the segmented atlas and a full fMRI analysis is possible

Physics-informed Neural Networks for Solving Inverse Problems of Nonlinear Biot's Equations: Batch Training

In biomedical engineering, earthquake prediction, and underground energy harvesting, it is crucial to indirectly estimate the physical properties of porous media since the direct measurement of those are usually impractical/prohibitive. Here we apply the physics-informed neural networks to solve the inverse problem with regard to the nonlinear Biot's equations. Specifically, we consider batch training and explore the effect of different batch sizes. The results show that training with small batch sizes, i.e., a few examples per batch, provides better approximations (lower percentage error) of the physical parameters than using large batches or the full batch. The increased accuracy of the physical parameters, comes at the cost of longer training time. Specifically, we find the size should not be too small since a very small batch size requires a very long training time without a corresponding improvement in estimation accuracy. We find that a batch size of 8 or 32 is a good compromise, which is also robust to additive noise in the data. The learning rate also plays an important role and should be used as a hyperparameter.Comment: arXiv admin note: text overlap with arXiv:2002.0823

Correlated Multimodal Imaging in Life Sciences:Expanding the Biomedical Horizon

International audienceThe frontiers of bioimaging are currently being pushed toward the integration and correlation of several modalities to tackle biomedical research questions holistically and across multiple scales. Correlated Multimodal Imaging (CMI) gathers information about exactly the same specimen with two or more complementary modalities that-in combination-create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows to describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, diseases or organisms by untangling their molecular mechanisms within their native environment. The two best-established CMI implementations for small animals and model organisms are hardware-fused platforms in preclinical imaging (Hybrid Imaging) and Correlated Light and Electron Microscopy (CLEM) in biological imaging. Although the merits of Preclinical Hybrid Imaging (PHI) and CLEM are well-established, both approaches would benefit from standardization of protocols, ontologies and data handling, and the development of optimized and advanced implementations. Specifically, CMI pipelines that aim at bridging preclinical and biological imaging beyond CLEM and PHI are rare but bear great potential to substantially advance both bioimaging and biomedical research. CMI faces three mai

High performance computing for 3D image segmentation

Digital image processing is a very popular and still very promising eld of science, which has been successfully applied to numerous areas and problems, reaching elds like forensic analysis, security systems, multimedia processing, aerospace, automotive, and many more. A very important part of the image processing area is image segmentation. This refers to the task of partitioning a given image into multiple regions and is typically used to locate and mark objects and boundaries in input scenes. After segmentation the image represents a set of data far more suitable for further algorithmic processing and decision making. Image segmentation algorithms are a very broad eld and they have received signi cant amount of research interest A good example of an area, in which image processing plays a constantly growing role, is the eld of medical solutions. The expectations and demands that are presented in this branch of science are very high and dif cult to meet for the applied technology. The problems are challenging and the potential bene ts are signi cant and clearly visible. For over thirty years image processing has been applied to different problems and questions in medicine and the practitioners have exploited the rich possibilities that it offered. As a result, the eld of medicine has seen signi cant improvements in the interpretation of examined medical data. Clearly, the medical knowledge has also evolved signi cantly over these years, as well as the medical equipment that serves doctors and researchers. Also the common computer hardware, which is present at homes, of ces and laboratories, is constantly evolving and changing. All of these factors have sculptured the shape of modern image processing techniques and established in which ways it is currently used and developed. Modern medical image processing is centered around 3D images with high spatial and temporal resolution, which can bring a tremendous amount of data for medical practitioners. Processing of such large sets of data is not an easy task, requiring high computational power. Furthermore, in present times the computational power is not as easily available as in recent years, as the growth of possibilities of a single processing unit is very limited - a trend towards multi-unit processing and parallelization of the workload is clearly visible. Therefore, in order to continue the development of more complex and more advanced image processing techniques, a new direction is necessary. A very interesting family of image segmentation algorithms, which has been gaining a lot of focus in the last three decades, is called Deformable Models. They are based on the concept of placing a geometrical object in the scene of interest and deforming it until it assumes the shape of objects of interest. This process is usually guided by several forces, which originate in mathematical functions, features of the input images and other constraints of the deformation process, like object curvature or continuity. A range of very desired features of Deformable Models include their high capability for customization and specialization for different tasks and also extensibility with various approaches for prior knowledge incorporation. This set of characteristics makes Deformable Models a very ef cient approach, which is capable of delivering results in competitive times and with very good quality of segmentation, robust to noisy and incomplete data. However, despite the large amount of work carried out in this area, Deformable Models still suffer from a number of drawbacks. Those that have been gaining the most focus are e.g. sensitivity to the initial position and shape of the model, sensitivity to noise in the input images and to awed input data, or the need for user supervision over the process. The work described in this thesis aims at addressing the problems of modern image segmentation, which has raised from the combination of above-mentioned factors: the signi cant growth of image volumes sizes, the growth of complexity of image processing algorithms, coupled with the change in processor development and turn towards multi-processing units instead of growing bus speeds and the number of operations per second of a single processing unit. We present our innovative model for 3D image segmentation, called the The Whole Mesh Deformation model, which holds a set of very desired features that successfully address the above-mentioned requirements. Our model has been designed speci cally for execution on parallel architectures and with the purpose of working well with very large 3D images that are created by modern medical acquisition devices. Our solution is based on Deformable Models and is characterized by a very effective and precise segmentation capability. The proposed Whole Mesh Deformation (WMD) model uses a 3D mesh instead of a contour or a surface to represent the segmented shapes of interest, which allows exploiting more information in the image and obtaining results in shorter times. The model offers a very good ability for topology changes and allows effective parallelization of work ow, which makes it a very good choice for large data-sets. In this thesis we present a precise model description, followed by experiments on arti cial images and real medical data

Registration and Analysis of Developmental Image Sequences

Mapping images into the same anatomical coordinate system via image registration is a fundamental step when studying physiological processes, such as brain development. Standard registration methods are applicable when biological structures are mapped to the same anatomy and their appearance remains constant across the images or changes spatially uniformly. However, image sequences of animal or human development often do not follow these assumptions, and thus standard registration methods are unsuited for their analysis. In response, this dissertation tackles the problems of i) registering developmental image sequences with spatially non-uniform appearance change and ii) reconstructing a coherent 3D volume from serially sectioned images with non-matching anatomies between the sections. There are three major contributions presented in this dissertation. First, I develop a similarity metric that incorporates a time-dependent appearance model into the registration framework. The proposed metric allows for longitudinal image registration in the presence of spatially non-uniform appearance change over time—a common medical imaging problem for longitudinal magnetic resonance images of the neonatal brain. Next, a method is introduced for registering longitudinal developmental datasets with missing time points using an appearance atlas built from a population. The proposed method is applied to a longitudinal study of young macaque monkeys with incomplete image sequences. The final contribution is a template-free registration method to reconstruct images of serially sectioned biological samples into a coherent 3D volume. The method is applied to confocal fluorescence microscopy images of serially sectioned embryonic mouse brains.Doctor of Philosoph