Anatomie | EPFL Graph Search

vignette|Illustration du traité d'anatomie De humani corporis fabrica d'André Vésale. L’ (emprunté au bas latin anatomia « dissection », issu du grec ἀνατέμνω (ànatémno), de ἀνά – ana, « en remontant », et τέμνω – temnō, « couper ») est la science qui décrit la forme et la structure des organismes vivants et les rapports des organes et tissus qui les constituent. On peut notamment distinguer l'anatomie animale (et en particulier l'anatomie humaine, très importante en médecine) et l'anatomie végétale (branche de la botanique). Définition Dérivé du grec ἀνατομή (anatomē, dissection), l'anatomie est l'étude scientifique de la structure des organismes, y compris leurs systèmes, organes et tissus. Elle analyse la morphologie et la position des différentes parties du corps, ainsi que leurs relations entre elles. L'anatomie est distincte de la physiologie et de la biochimie, qui traitent respectivement des fonctions de ces parties et des processus chimiques impliqués. Par exemple

Structural architecture of the Neuronal-Glial-Vascular system

Eleftherios Zisis

Brain functionality relies on the neuronal-glial-vascular (NGV) ensemble for energy support. However, the details of the complex biological mechanisms involved in these processes and the microscopic interactions between these three components are not yet fully understood. Astrocytes, an essential component of the NGV ensemble, extend ramified processes to the vasculature and to neuronal synapses to provide neuronal energy support, homeostatic control, and multi-directional signaling with the vasculature, neurons, and neighboring astrocytes. As key links between the components of the NGV circuitry, the astrocytes are essential for drug delivery in the brain and are involved in the progression of neurodegenerative diseases. Therefore, comprehensive models of the neuronal-glial-vascular (NGV) ensemble are essential for understanding the role of astrocytes in the formation and function of the complex networks within the brain and its pathological conditions. A complete computational model of this ensemble has not yet been developed. This thesis presents the first model of a data-driven digital reconstruction of the neuronal-glial-vascular (NGV) ensemble at a micrometer anatomical resolution. Combining the sparse literature data and the few available detailed biological reconstructions, I have computationally generated the structural architecture of a neocortical NGV circuit that forms a functional column of the juvenile rat neocortex and consists of neurons, protoplasmic astrocytes, and the microvasculature, including their pairwise connectivities. This data-driven approach allows for incremental refinement as more experimental data become available, new biophysical models get published, and new questions arise. The NGV circuit is validated against a plethora of literature sources to ensure its biological fidelity: it successfully reproduces the spatial organization of the astrocytes, their morphological characteristics as well as their volume occupancy, and the overlap with their neighbors. The power of the computational model of NGV lies in its ability to serve as a framework for addressing long-standing questions that cannot be experimentally investigated due to the complexity of the microscopic systems and the limitations of current techniques to observe all components simultaneously. In this thesis, I have performed experiments to investigate why astrocytes acquire their particular shapes, the organizational principles of NGV that lead to the observed biological network architectures, and the effect of astrocytic density on endfoot organization. The circuit's structural analysis showed that astrocytes optimize their positions and spacing from each other to provide the vasculature with a uniform coverage for trophic support and signaling. By increasing the density of astrocytes in NGV circuits, I discovered a limit in astrocyte's ability to make perivascular projections because of the vascular spatial occupancy, which constrains the extent of astrocyte morphology. Thus, their role in linking vasculature to neurons constrains their organization via the proportional relationship of density and microdomain shrinkage due to contact spacing. By addressing these questions, I demonstrated how this model can serve as an exploratory tool that provides a window into the complexity of the NGV architecture.

EPFL2021

Breast texture synthesis and estimation of the role of the anatomy and tumor shape in the radiological detection process

Cyril Castella

Breast cancer is the most common, and the number one cause of death by cancer among women. However, when it is sufficiently early detected, heavy treatments can be avoided, and morbidity and mortality can be reduced. This is the reason why randomized clinical trials were started in the 60s, followed in the last decades by screening mammography national programs. The process of breast cancer detection in mammography is complex. Its understanding offers numerous challenges to radiologists and medical physicists. One way to apprehend it is to model this process step-by-step by performing psychophysical experiments with anatomical or synthetic images. In this approach, the images information content is controlled. Since the first experiments with synthetic images created with white noise and simple geometric signals, technical and computational improvements allowed to get ever closer to clinical realism for studying the mechanism of perception of a signal on a radiological image. The present work extends the list of tools that have been used until now in psychophysical experiments in mammography. It proposes a detailed statistical analysis of anatomical images, from which algorithms for breast density classification and realistic breast texture synthesis are developed. In a second phase, psychophysical experiments with simple signals and benign or malignant masses combined with anatomical or synthetic backgrounds are presented. The performance of human observers is analyzed as a function of parameters such as background type, signal type, or uncertainty about the size or the shape of the signal. These results are compared to that of existing or adapted models from the literature, and the different models are evaluated in their ability to predict the performance of human observers for the detection of lesions in such conditions. Each step of this project focused on the objective and reproducible aspect of the image evaluation or of the observer performance. Controlled yet realistic conditions ensure the robustness of the results, as well as their clinical adaptability. Among the main results, synthetic mammographic texture images have been generated and validated. They provide a virtually unlimited database of images with demonstrated visual and statistical realism. Concerning the analysis of the human observers' performance, this work shows that they are sensitive to uncertainty about the signal size but not about its shape, that they use similar detection strategies with real and synthetic images, and that they are mainly sensitive to the anatomical fluctuations in the immediate area around the signal location. These effects, as well as the performance level of human observers for the detection tasks under consideration, could be reproduced by models taking into account human visual system characteristics. The conclusions of this work will be able to guide future studies in the field of detection tasks in mammography or tomosynthesis. This 3D breast imaging technique presents, like mammography, numerous challenges in order to understand and to objectively characterize its clinical potential. Studies with model observers specifically validated for detection tasks in medical imaging provide an excellent alternative in terms of time and costs for answering these questions.

EPFL2009

Soft, Implantable Bioelectronic Interfaces for Translational Research

Beatrice Barra, Jocelyne Bloch, Simon Borgognon, Marco Capogrosso, Grégoire Courtine, Florian Dylan Fallegger, Ivan Furfaro, Sébastien Jiguet, Xiaoyang Kang, Stéphanie Lacour, Evgenia Vladimirova Roussinova, Andreas Rowald, Giuseppe Schiavone, Ismael Seanez Gonzalez, Nicolas Vachicouras

The convergence of materials science, electronics, and biology, namely bioelectronic interfaces, leads novel and precise communication with biological tissue, particularly with the nervous system. However, the translation of lab‐based innovation toward clinical use calls for further advances in materials, manufacturing and characterization paradigms, and design rules. Herein, a translational framework engineered to accelerate the deployment of microfabricated interfaces for translational research is proposed and applied to the soft neurotechnology called electronic dura mater, e‐dura. Anatomy, implant function, and surgical procedure guide the system design. A high‐yield, silicone‐on‐silicon wafer process is developed to ensure reproducible characteristics of the electrodes. A biomimetic multimodal platform that replicates surgical insertion in an anatomy‐based model applies physiological movement, emulates therapeutic use of the electrodes, and enables advanced validation and rapid optimization in vitro of the implants. Functionality of scaled e‐dura is confirmed in nonhuman primates, where epidural neuromodulation of the spinal cord activates selective groups of muscles in the upper limbs with unmet precision. Performance stability is controlled over 6 weeks in vivo. The synergistic steps of design, fabrication, and biomimetic in vitro validation and in vivo evaluation in translational animal models are of general applicability and answer needs in multiple bioelectronic designs and medical technologies.

2020