Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation

Automated morphometry for mouse brain MRI through structural parcellation and thickness estimation

Quantitative morphometric analysis is an important tool in neuroimaging for the study of understanding the physiology of development, normal aging, disease pathology and treatment effect. However, compared to clinical study, image analysis methods specific to preclinical neuroimaging are still lacking. The aim of this PhD thesis is to achieve automatic quantitative structural analysis of mouse brain MRI. This thesis focuses on two quantitative methods which have been widely accepted as quantitative imaging biomarkers: brain structure segmentation and cortical thickness estimation. Firstly, a multi-atlas based structural parcellation framework has been constructed, which incorporates preprocessing steps such as intensity non-uniformity correction and multi-atlas based brain extraction, followed by non-rigid registration and local weighted multi-atlas label fusion. Validation of the framework demonstrated improved performance compared to single-atlas-based structural parcellation, as well as to global weighted multi-atlas label fusion methods. The framework has been further applied to in vivo and ex vivo data acquired from the same cohort so that the respective volumetric analysis can be compared. The results reveal a non-uniform distribution of volume changes from the in vivo to the post-mortem brain. In addition, volumetric analysis based on the segmented structures showed similar statistical power on in vivo or ex vivo data within the same cohort. Secondly, a framework to segment the mouse cerebellar cortex sublayers from brain MRI data and estimate the thickness of the corresponding layers has been developed. Application of the framework on the experimental data demonstrated its ability to distinguish sublayer thickness variation between transgenic strains and their wild-type littermate, which cannot be detected using full cortical thickness measurements alone. In conclusion, two quantitative morphometric analysis frameworks have been pre-sented in this thesis. This demonstrated the successful application of translational quantitative methods to preclinical mouse brain MRI