Dense deformation field estimation for atlas registration using the active contour framework

A key research area in computer vision is image segmentation. Image segmentation aims at extracting objects of interest in images or video sequences. These objects contain relevant information for a given application. For example, a video surveillance application generally requires to extract moving objects (vehicles, persons or animals) from a sequence of images in order to check that their path stays conformed to the regulation rules set for the observed scene. Image segmentation is not an easy task. In many applications, the contours of the objects of interest are difficult to delineate, even manually. The problems linked to segmentation are often due to low contrast, fuzzy contours or too similar intensities with adjacent objects. In some cases, the objects to be extracted have no real contours in the image. This kind of objects is called virtual objects. Virtual objects appear especially in medical applications. To draw them, medical experts usually estimate their position from surrounding objects. The problems related to image segmentation can be greatly simplified with information known in advance on the objects to be extracted (the prior knowledge). A widely used method consists to extract the needed prior knowledge from a reference image often called atlas. The goal of the atlas is to describe the image to be segmented like a map would describe the components of a geographical area. An atlas can contain three types of information on each object being part of the image: an estimation of its position in the image, a description of its shape and texture, and the features of its adjacent objects. The atlas-based segmentation method is rather used when the atlas can characterize a range of images. This method is thus especially adapted to medical images due to the existing consistency between anatomical structures of same type. There exist two types of atlas: the determinist atlas and the statistical atlas. The determinist atlas is an image which has been selected or computed, to be the most representative of an image category to be segmented. This image is called intensity atlas. The contours of the objects of interest (the objects to be extracted in images of the same type) have been traced manually on the intensity atlas, or by using a semi-automatic method. A label is often attributed to each one of these objects in order to differentiate them. In this way, we obtain a labeled version of the atlas called labeled atlas. The statistical atlas is an atlas created from a database of images in order to be the most representative of a certain type of images to be segmented. In this atlas, the position and the features of the objects of interest depend on statistical measures. In this thesis, we are focused on the use of determinist atlases for image segmentation. The segmentation process with a determinist atlas consists to deform the objects delineated in the atlas in order to better align them with their corresponding objects in the image to be segmented. To perform this task, we have distinguished two types of approaches in the literature. The first approach consists to reduce the segmentation problem in an image registration problem. First of all, a dense deformation field that registers (i.e. puts in point-to-point spatial correspondence) the atlas to the image to be segmented, is explicitly computed. Then, this transformation is used to project the assigned labels onto each atlas structure on the image to be segmented. The advantage of this approach is that the deformation field computed from the registration of visible contours allows to easily estimate the position of virtual objects or objects with fuzzy contours. However, the methods currently used for the atlas registration are often only based on the intensity atlas. That means that they do not exploit the object-based information that can be obtained by combining the intensity atlas with its labeled version. In the second approach, the atlas contours selected by the labeled atlas are directly deformed without using a geometrical deformation. For that, this approach is based on matching contour techniques, generally called deformable models. In this thesis, we are interested to a particular type of deformable models, which are the active contour segmentation models. The advantage of the active contour method is that this segmentation technique has been designed to exploit the image information directly linked to the object to be delineated. By using object-based information, active contour models are frequently able to extract regions where the atlas-based segmentation method by registration fails. On the other hand, the result of this local segmentation method is very sensitive to the initial atlas contour position regarding to the target contours. On the other hand, this local segmentation method is very sensitive to the initial position of the atlas contours: the closer they are to the contours to be detected, the more robust the active contour-based segmentation will be. Besides, this segmentation technique needs prior shape models to be able to estimate the position of virtual objects. The main objective of this thesis is to design an algorithm for atlas-based segmentation which combines the advantages of the dense deformation field computed by the registration algorithms, with local segmentation constraints coming from the active contour framework. This implies to design a model where the registration and segmentation by active contours are jointly performed. The atlas registration algorithm that we propose is based on a formulation allowing the integration of any segmentation or contour regularization forces derived from the theory of the active contours in a non parametric registration process. Our algorithm led us to introduce the concept of hierarchical atlas registration. Its principle is that the registration of the main image objects helps the registration of depending objects. This allows to bring progressively the atlas contours closer to their target and thus, to limit the risk to be stuck in a local minimum. Our model had been designed to be easily adaptable to various types of segmentation problems. At the end of the thesis, we present several examples of atlas registration applications in medical imaging. These applications highlight the integration of manual constraints in an atlas registration process, the modeling of a tumor growth in the atlas, the labelization of the thalamus for a statistical study on neuronal connections, the localization of the subthalamic nucleus (STN) for deep brain stimulation (DBS) and the compensation of intra-operative brain shift for neuronavigation systems

The effects of DMT and associated psychedelics on the human mind and brain

This work presents seven investigations conducted with the aim to determine the effects of DMT (a compound which is able to cause remarkable effects in consciousness) and associated psychedelic drugs on the human brain and mind. Including a variety of neuroimaging (EEG, MEG and fMRI), phenomenological, psychometric and naturalistic research methods, these are the first controlled investigations of the impact of DMT in the human resting brain. Results revealed that DMT disrupted several brain mechanisms associated with top-down control (alpha power, integrity of high-level networks, modularity), increased measures related to entropy, or disorder (Lempel-Ziv complexity, novel pairwise connectivity) and immersive states of consciousness (delta/theta power), with some of these effects following the experiential trajectories of the DMT state. We also observed a significant temporal correlation between some of these effects (alpha power and default-mode network integrity fluctuations), which were supported by LSD effects of reduced feedback connectivity and neural adaption mechanisms. suggesting that the psychedelic brain state is one of reduced modularity, increased integration and functional plasticity. These findings were complemented by psychological studies showing that the DMT state is one of immersive visual imagery, intense somatic experiences and partial disconnection from the environment, which we found shared significant overlap with near- death experiences. DMT administration also resulted in positive mental health outcomes in healthy volunteers providing evidence for the first time that DMT may provide a useful alternative to currently- investigated psychedelic treatments. Finally, results from our last study performed in naturalistic environments revealed that psychedelics are able to have a transformative potential on core beliefs concerning the fundamental nature of reality and consciousness for up to 6 months, with important social and bioethical implications. Collectively these results attest to the strong impact that psychedelics have on varied human domains, which range experience, brain activity, mental health, intersubjectivity and beliefs.Open Acces

Development and application of functional MRI methods to investigate brainstem haemodynamics in the context of systemic hypertension

The selfish brain mechanism proposes that in some cases hypertension could develop as a compensatory mechanism that aims to maintain cerebral blood flow (CBF) by increasing systemic blood pressure through an increase in cardiovascular sympathetic tone. The mechanism that might trigger this hypothesised initial reduction in CBF is uncertain, but the brainstem is an important component of the central autonomic nervous system and may therefore play an important role in the development of hypertension via the selfish brain mechanism. Various techniques have been used to investigate the selfish brain mechanism in humans, including magnetic resonance imaging (MRI) methods to measure CBF and cerebrovascular reactivity (CVR). CVR quantifies the change in CBF in response to a vascular stimulus, and is related to the responsiveness, tone and functional reserve of the cerebrovascular system. This thesis aims to validate, optimise and apply a variety of MRI-based methods of quantifying human cerebrovascular function, which may then be used in future studies to further investigate the selfish brain mechanism. Firstly, methods of measuring CBF and CVR using MRI are tailored towards their application in the brainstem and the feasibility of measuring regional brainstem CBF and CVR is demonstrated. Next, existing data is explored to study the association between vertebral artery hypoplasia (VAH) and brainstem CBF in hypertensives but no statistically significant association between regional CBF, VAH and hypertension is found. Brainstem co-registration is then optimised using machine learning. The UK biobank dataset is explored to study the amplitude of low-frequency fluctuations (ALFF) in the BOLD signal, a potential surrogate index of CVR, in hypertensives. There is no statistically significant difference in the regional variation in ALFF between hypertensives and normotensives. Following this, the relationship between ALFF and CVR is investigated to validate ALFF as a surrogate marker of CVR, but no evidence to support the use of ALFF as a specific metric of CVR is demonstrated. Finally, a pilot study of functional MRI in the locus coeruleus, an important noradrenergic brainstem nucleus that is integral to the central autonomic network, is undertaken. The feasibility of mapping functional connectivity of the LC using an anatomical localiser tailored to each participant is demonstrated

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

[¹⁸F]fluorination of biorelevant arylboronic acid pinacol ester scaffolds synthesized by convergence techniques

Aim: The development of small molecules through convergent multicomponent reactions (MCR) has been boosted during the last decade due to the ability to synthesize, virtually without any side-products, numerous small drug-like molecules with several degrees of structural diversity.(1) The association of positron emission tomography (PET) labeling techniques in line with the “one-pot” development of biologically active compounds has the potential to become relevant not only for the evaluation and characterization of those MCR products through molecular imaging, but also to increase the library of radiotracers available. Therefore, since the [18F]fluorination of arylboronic acid pinacol ester derivatives tolerates electron-poor and electro-rich arenes and various functional groups,(2) the main goal of this research work was to achieve the 18F-radiolabeling of several different molecules synthesized through MCR. Materials and Methods: [18F]Fluorination of boronic acid pinacol esters was first extensively optimized using a benzaldehyde derivative in relation to the ideal amount of Cu(II) catalyst and precursor to be used, as well as the reaction solvent. Radiochemical conversion (RCC) yields were assessed by TLC-SG. The optimized radiolabeling conditions were subsequently applied to several structurally different MCR scaffolds comprising biologically relevant pharmacophores (e.g. β-lactam, morpholine, tetrazole, oxazole) that were synthesized to specifically contain a boronic acid pinacol ester group. Results: Radiolabeling with fluorine-18 was achieved with volumes (800 μl) and activities (≤ 2 GBq) compatible with most radiochemistry techniques and modules. In summary, an increase in the quantities of precursor or Cu(II) catalyst lead to higher conversion yields. An optimal amount of precursor (0.06 mmol) and Cu(OTf)2(py)4 (0.04 mmol) was defined for further reactions, with DMA being a preferential solvent over DMF. RCC yields from 15% to 76%, depending on the scaffold, were reproducibly achieved. Interestingly, it was noticed that the structure of the scaffolds, beyond the arylboronic acid, exerts some influence in the final RCC, with electron-withdrawing groups in the para position apparently enhancing the radiolabeling yield. Conclusion: The developed method with high RCC and reproducibility has the potential to be applied in line with MCR and also has a possibility to be incorporated in a later stage of this convergent “one-pot” synthesis strategy. Further studies are currently ongoing to apply this radiolabeling concept to fluorine-containing approved drugs whose boronic acid pinacol ester precursors can be synthesized through MCR (e.g. atorvastatin)