Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Quantitative evaluation of atlas-based highdensity diffuse optical tomography for imaging of the human visual cortex

Image recovery in diffuse optical tomography (DOT) of the human brain often relies on accurate models of light propagation within the head. In the absence of subject specific models for image reconstruction, the use of atlas based models are showing strong promise. Although there exists some understanding in the use of some limited rigid model registrations in DOT, there has been a lack of a detailed analysis between errors in geometrical accuracy, light propagation in tissue and subsequent errors in dynamic imaging of recovered focal activations in the brain. In this work 11 different rigid registration algorithms, across 24 simulated subjects, are evaluated for DOT studies in the visual cortex. Although there exists a strong correlation (R(2) = 0.97) between geometrical surface error and internal light propagation errors, the overall variation is minimal when analysing recovered focal activations in the visual cortex. While a subject specific mesh gives the best results with a 1.2 mm average location error, no single algorithm provides errors greater than 4.5 mm. This work demonstrates that the use of rigid algorithms for atlas based imaging is a promising route when subject specific models are not available

Anchoring The Cognitive Map To The Visual World

To interact rapidly and effectively with the environment, the mammalian brain needs a representation of the spatial layout of the external world (or a “cognitive map”). A person might need to know where she is standing to find her way home, for instance, or might need to know where she is looking to reach for her out-of-sight keys. For many behaviors, however, simply possessing a map is not enough; in order for a map to be useful in a dynamic world, it must be anchored to stable environmental cues. The goal of the present research is to address this spatial anchoring problem in two different domains: navigation and vision. In the first part of the thesis, which comprises Chapters 1-3, we examine how navigators use perceptual information to re-anchor their cognitive map after becoming lost, a process known as spatial reorientation. Using a novel behavioral paradigm with rodents, in Chapter 2 we show that the cognitive map is reoriented by dissociable inputs for identifying where one is and recovering which way one is facing. The findings presented in Chapter 2 also highlight the importance of environmental boundaries, such as the walls of a room, for anchoring the cognitive map. We thus predicted that there might exist a brain region that is selectively involved in boundary perception during navigation. Accordingly, in Chapter 3, we combine transcranial magnetic stimulation and virtual-reality navigation to reveal the existence of such a boundary perception region in humans. In the second part of this thesis, Chapter 4, we explore whether the same mechanisms that support the cognitive map of navigational space also mediate a map of visual space (i.e., where one is looking). Using functional magnetic resonance imaging and eye tracking, we show that human entorhinal cortex supports a map-like representation of visual space that obeys the same principles of boundary-anchoring previously observed in rodent maps of navigational space. Together, this research elucidates how mental maps are anchored to the world, thus allowing the mammalian brain to form durable spatial representations across body and eye movements

Intersubject Regularity in the Intrinsic Shape of Human V1

Previous studies have reported considerable intersubject variability in the three-dimensional geometry of the human primary visual cortex (V1). Here we demonstrate that much of this variability is due to extrinsic geometric features of the cortical folds, and that the intrinsic shape of V1 is similar across individuals. V1 was imaged in ten ex vivo human hemispheres using high-resolution (200 μm) structural magnetic resonance imaging at high field strength (7 T). Manual tracings of the stria of Gennari were used to construct a surface representation, which was computationally flattened into the plane with minimal metric distortion. The instrinsic shape of V1 was determined from the boundary of the planar representation of the stria. An ellipse provided a simple parametric shape model that was a good approximation to the boundary of flattened V1. The aspect ration of the best-fitting ellipse was found to be consistent across subject, with a mean of 1.85 and standard deviation of 0.12. Optimal rigid alignment of size-normalized V1 produced greater overlap than that achieved by previous studies using different registration methods. A shape analysis of published macaque data indicated that the intrinsic shape of macaque V1 is also stereotyped, and similar to the human V1 shape. Previoud measurements of the functional boundary of V1 in human and macaque are in close agreement with these results

Efforts in Preparation for Jack Validation

This document presents a detailed record of the methodologies, assumptions, limitations, and references used in creating the human figure model in Jack, a program that displays and manipulates articulated geometric figures. This report reflects current efforts to develop and refine Jack software to enable its validation and verification as a tool for performing human engineering analysis. These efforts include human figure model improvements, statistical anthropometric data processing methods, enhanced human figure model construction and measuring methods, and automated accomodation analysis. This report discusses basic details of building human models, model anthropometry, scaling, Jack anthropometry-based human models, statistical data processing, figure generation tools, anthropometric errors, inverse dynamics, smooth skin implementation, guidelines used in estimating landmark locations on the model, and recommendations for validating and verifying the Jack human figure model

Towards an early 3D-diagnosis of craniofacial asymmetry by computing the accurate midplane: A PCA-based method

Background and objective: Craniofacial asymmetry is a common growth disorder often caused by unilateral chewing. Although an early orthodontic treatment would avoid surgical procedures later in life, the uncertainty of defining the accurate sagittal midplane potentially leads to misdiagnosis and therefore inaccurate orthodontic treatment plans. This novel study aims to 3D-diagnose craniofacial complex malformations in children with unilateral crossbite (UXB) considering a midplane which compensates the asymmetric morphology. Methods: The sagittal midplane of 20 children, fifteen of whom exhibited UXB, was computed by a PCA- based method which compensates the asymmetry mirroring the 3D models obtained from cone-beam computed tomography data. Once determined, one side of the data was mirrored using the computed midplane to visualize the malformations on the hard and soft tissues by 3D-computing the distances between both halves. Additionally, 31 skull’s landmarks were manually placed in each model to study the principal variation modes and the significant differences in the group of subjects with and without UXB through PCA and Mann-Whitney U test analyses respectively. Results: Morphological 3D-analysis showed pronounced deformities and aesthetic implications for patients with severe asymmetry (jaw deviation > 0.8 mm) in whole craniofacial system, while initial signs of asymmetry were found indistinctly in the mandible or maxilla. We detected significant ( p < 0.05) malformations for example in mandibular ramus length (0.0086), maxillary palate width (0.0481) and condylar head width (0.0408). Craniofacial malformations increased the landmarks’ variability in the group of patients with UXB over the control group requiring 8 variation modes more to define 99% of the sample’ variability. Conclusions: Our findings demonstrated the viability of early diagnosis of craniofacial asymmetry through computing the accurate sagittal midplane which compensates the individual’s asymmetrical morphology. Furthermore, this study provides important computational insights into the determination of craniofacial deformities which are caused by UXB, following some empirical findings of previous clinical studies. Hence, this computational approach can be useful for the development of new software in craniofacial surgery or for its use in biomedical research and clinical practice

Robust Algorithms for Registration of 3D Images of Human Brain

This thesis is concerned with the process of automatically aligning 3D medical images of human brain. It concentrates on rigid-body matching of Positron Emission Tomography images (PET) and Magnetic Resonance images (MR) within one patient and on non-linear matching of PET images of different patients. In recent years, mutual information has proved to be an excellent criterion for automatic registration of intra-individual images from different modalities. We propose and evaluate a method that combines a multi-resolution optimization of mutual information with an efficient segmentation of background voxels and a modified principal axes algorithm. We show that an acceleration factor of 6-7 can be achieved without loss of accuracy and that the method significantly reduces the rate of unsuccessful registrations. Emphasis was also laid on creation of an automatic registration system that could be used routinely in clinical environment. Non-linear registration tries to reduce the inter-individual variability of shape and structure between two brain images by deforming one image so that homologous regions in both images get aligned. It is an important step of many procedures in medical image processing and analysis. We present a novel algorithm for an automatic non-linear registration of PET images based on hierarchical volume subdivisions and local affine optimizations. It produces a C2-continuous deformation function and guarantees that the deformation is one-to-one. Performance of the algorithm was evaluated on more than 600 clinical PET images

AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses

Facial landmark localization aims to detect the predefined points of human faces, and the topic has been rapidly improved with the recent development of neural network based methods. However, it remains a challenging task when dealing with faces in unconstrained scenarios, especially with large pose variations. In this paper, we target the problem of facial landmark localization across large poses and address this task based on a split-and-aggregate strategy. To split the search space, we propose a set of anchor templates as references for regression, which well addresses the large variations of face poses. Based on the prediction of each anchor template, we propose to aggregate the results, which can reduce the landmark uncertainty due to the large poses. Overall, our proposed approach, named AnchorFace, obtains state-of-the-art results with extremely efficient inference speed on four challenging benchmarks, i.e. AFLW, 300W, Menpo, and WFLW dataset. Code will be available at https://github.com/nothingelse92/AnchorFace.Comment: To appear in AAAI 202

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