Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI

Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning-based registration algorithms, including joint optimization approaches, currently suffer from bias due to selection of a fixed reference frame and only support pairwise transformations. We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations. We evaluate the proposed method on longitudinal analysis of a white matter tract in a brain MRI dataset with 2-3 time-points for 3249 individuals, i.e., 8045 images in total. The reproducibility of the method is evaluated on test-retest data from 97 individuals. The results confirm that the implicit reference image is an average of the input image. In addition, the proposed framework leads to consistent segmentations and significantly lower processing bias than that of a pair-wise fixed-reference approach. This processing bias is even smaller than those obtained when translating segmentations by only one voxel, which can be attributed to subtle numerical instabilities and interpolation. Therefore, we postulate that the proposed mean-space learning strategy could be widely applied to learning-based registration tasks. In addition, this group-wise framework introduces a novel way for learning-based longitudinal studies by direct construction of an unbiased within-subject template and allowing reliable and efficient analysis of spatio-temporal imaging biomarkers.Comment: SPIE Medical Imaging 2021 (oral

Registration of longitudinal brain image sequences with implicit template and spatial–temporal heuristics

Accurate measurement of longitudinal changes of brain structures and functions is very important but challenging in many clinical studies. Also, across-subject comparison of longitudinal changes is critical in identifying disease-related changes. In this paper, we propose a novel method to meet these two requirements by simultaneously registering sets of longitudinal image sequences of different subjects to the common space, without assuming any explicit template. Specifically, our goal is to 1) consistently measure the longitudinal changes from a longitudinal image sequence of each subject, and 2) jointly align all image sequences of different subjects to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial-temporal behavior of anatomical changes in each longitudinal image sequence. Then, a probabilistic model is built upon the temporal fibers to characterize both spatial smoothness and temporal continuity. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of the probabilistic models. Promising results have been obtained in quantitative measurement of longitudinal brain changes, i.e., hippocampus volume changes, showing better performance than those obtained by either the pairwise or the groupwise only registration methods

Analyzing shape and residual pose of subcortical structures in brains of subjects with schizophrenia

This study focuses on four anatomical features of subcortical structures associated with schizophrenia: volume, surface area, shape and residual pose. Being a chronic mental disorder, schizophrenia affects 1% of the local population and is one of the leading causes of disability around the world. However, the symptoms of schizophrenia appear and spread gradually, and robust mathematical and statistical models of disease progression have the capability to help find meaningful biomarkers of schizophrenia, which may aid researchers and clinicians to develop potentially novel treatments of the disease. This study used the open-source Schizconnect dataset, and data was automatically segmented by the MRICloud pipeline, following which scans were mapped to a common surface template using unbiased diffeomorphic mapping. The first part of this study focuses on global volumetric and local surface analysis of 6 subcortical structures; the Amygdala, the Hippocampus, the Caudate, the Putamen, the Globus Pallidum, and the Thalamus. Significant total volume and regional surface area changes are seen in the hippocampus and thalamus, and reduced atrophy is seen in the diseased subjects compared to the control subjects for the hippocampus, globus pallidum, and thalamus, whereas increased atrophy is seen for the diseased subjects compared to the control subjects in the amygdala, caudate and putamen. This study also develops a mathematical formulation for residual pose analysis, describing a robust algorithm to obtain residual pose parameters from MR scans using general orthogonalized Procrustes analysis, and modelling of rigid transformation matrices as Lie Groups. Cross-sectional and longitudinal analysis is performed on these residual pose parameters, and significant differences are seen in the amygdala, hippocampus, caudate and globus pallidum for the cross-sectional study, whereas significant changes are seen in the amygdala, hippocampus, and caudate for the longitudinal study. This study aims to be the first known exploration of residual pose to characterize longitudinal development of schizophrenia and analyze complementary features to traditional shape analysis that have previously been discarded in the exploration of this disease, while also developing a robust mathematical formulation for pose analysis, in order to contribute to further research that has the potential to find biomarkers of disease onset and progression from non-invasive imaging modalities such as MRI

Unbiased diffeomorphic atlas construction for computational anatomy

pre-printConstruction of population atlases is a key issue in medical image analysis, and particularly in brain mapping. Large sets of images are mapped into a common coordinate system to study intra-population variability and inter-population differences, to provide voxel-wise mapping of functional sites, and help tissue and object segmentation via registration of anatomical labels. Common techniques often include the choice of a template image, which inherently introduces a bias. This paper describes a new method for unbiased construction of atlases in the large deformation diffeomorphic setting. A child neuroimaging autism study serves as a driving application. There is lack of normative data that explains average brain shape and variability at this early stage of development. We present work in progress toward constructing an unbiased MRI atlas of two year of children and the building of a probabilistic atlas of anatomical structures, here the caudate nucleus. Further, we demonstrate the segmentation of new subjects via atlas mapping. Validation of the methodology is performed by comparing the deformed probabilistic atlas with existing manual segmentations

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth.

Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth

Anatomical Image Series Analysis in the Computational Anatomy Random Orbit Model

Serially acquired medical imagery plays an important role in the computational study of human anatomy. In this work, we describe the development of novel algorithms set in the large deformation diffeomorphic metric mapping framework for analyzing serially acquired imagery of two general types: spatial image series and temporal image series. In the former case, a critical step in the analysis of neural connectivity from serially-sectioned brain histology data is the reconstruction of spatially distorted image volumes and registration into a common coordinate space. In the latter case, computational methods are required for building low dimensional representations of the infinite dimensional shape space standard to computational anatomy. Here, we review the vast body of work related to volume reconstruction and atlas-mapping of serially-sectioned data as well as diffeomorphic methods for longitudinal data and we position our work relative to these in the context of the computational anatomy random orbit model. We show how these two problems are embedded as extensions to the classic random orbit model and use it to both enforce diffeomorphic conditions and analyze the distance metric associated to diffeomorphisms. We apply our new algorithms to histology and MRI datasets to study the structure, connectivity, and pathological degeneration of the brain

Hierarchical unbiased graph shrinkage (HUGS): A novel groupwise registration for large data set

Normalizing all images in a large data set into a common space is a key step in many clinical and research studies, e.g., for brain development, maturation, and aging. Recently, groupwise registration has been developed for simultaneous alignment of all images without selecting a particular image as template, thus potentially avoiding bias in the registration. However, most conventional groupwise registration methods do not explore the data distribution during the image registration. Thus, their performance could be affected by large inter-subject variations in the data set under registration. To solve this potential issue, we propose to use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both infant and adult data sets, by also comparing with the conventional group-mean based registration and the ABSORB methods. All experimental results show that our proposed method can achieve better performance in terms of registration accuracy and robustness

Preclinical Alzheimer's Disease in the Entorhinal and Transentorhinal Cortex

Research on biomarkers of Alzheimer's disease has been shifting focus toward identifying changes in the preclinical stage, a stage prior to the emergence of cognitive deficits. Advances in the field of computational anatomy leverages noisy, longitudinal data for more sensitive and robust detection of shape differences. In particular, cortical thickness measures have been shown to be a sensitive marker of change. In this work, we introduce a pipeline for quantifying cortical thickness and develop three models to study the earliest changes detected from structural MRI. First, we investigate where grey matter atrophy occurs with great spatial resolution using a new cortical thickness metric and a mixed effects model of group differences. Next, we determine when grey matter atrophy begins using a piece-wise linear mixed effects model of atrophy. Finally, we characterize early progression of the disease in an individual using a subject-specific model of atrophy spread

CartiMorph: a framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.Comment: To be published in Medical Image Analysi