3D Segmentation of Soft Tissues by Flipping-free Mesh Deformation

Ph.DDOCTOR OF PHILOSOPH

Development of the VHP-Female Full-Body Computational Model and Its Applications for Biomedical Electromagnetic Modeling

Computational modeling offers better insight into a wide range of bioelectrical and biomechanical problems with improved tools for the design of medical devices and the diagnosis of pathologies. Electromagnetic modeling at low and high frequencies is particularly necessary. Modeling electromagnetic, structural, thermal, and acoustic response of the human body to different internal and external stimuli is limited by the availability of numerically efficient computational human models. This study describes the development to date of a computational full-body human model - Visible Human Project (VHP) - Female Model. Its unique feature is full compatibility both with MATLAB and specialized FEM computational software packages such as ANSYS HFSS/Maxwell 3D. This study also describes progress made to date in using the newly developed tools for segmentation. A visualization tool is implemented within MATLAB and is based on customized version of the constrained 2D Delaunay triangulation method for intersecting objects. This thesis applies a VHP - Female Model to a specific application, transcranial Direct Current Stimulation (tDCS). Transcranial Direct Current Stimulation has been beneficial in the stimulation of cortical activity and treatment of neurological disorders in humans. The placement of electrodes, which is cephalic versus extracephalic montages, is studied for optimal targeting of currents for a given functional area. Given the difficulty of obtaining in vivo measurements of current density, modeling of conventional and alternative electrode montages via the FEM has been utilized to provide insight into the tDCS montage performance. An insight into future work and potential areas of research, such as study of bone quality have been presented too

Semantic models for texturing volume objects

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Simulation of left-right asymmetry in heart morphogenesis

There are several studies on the heart morphogenesis in the vertebrate embryo, and in particular on how during the development of the heart tube bilateral symmetry is broken leading to morphogenesis with left-right asymmetry. Despite clinical and experimental findings, it is still not entirely clear how left-right patterning drives asymmetric morphogenesis, as the focus has generally been on a simple description of the direction of the loop. One way to overcome the conundrums in clinical research is to use predictive computational models to help explore shape variations during heart development, depending on the congenital anomaly to be studied. Heterotaxy, as a set of pathologies affecting the spatial structure of the heart due to left-right asymmetry (among others), can lead to cardiovascular diseases, so it is of particular relevance to find the origin of this anomaly and the different configurations that can lead to its emergence. One of them is known as "Transposition of the great arteries (TGA)" and is suspected to be due to a twist of the outflow tract (OFT) during morphogenesis. For this study we aimed to predict, through computational simulations and using discretization and finite element meshing methods, the morphogenesis of a heart model developed after the heart tube loop when the OFT region does not grow, mainly using the quantification of the twist angle. The results provide an insight into the mechanism of the cardiac loop, where the flipping tendency is to the right leading to a re-organization of the ventricles as the first finding. This is relevant for congenital heart defects as well as for the estimation of the left-right pattern in the morphogenesis of the heart in order to get a better classification in the different classes of heterotaxy syndrome.Incomin

Development of the VHP-Female CAD model including Dynamic Breathing Sequence

Mathematics, physics, biology, and computer science are combined to create computational modeling, which studies the behaviors and reactions of complex biomedical problems. Modern biomedical research relies significantly on realistic computational human models or “virtual humans�. Relevant study areas utilizing computational human models include electromagnetics, solid mechanics, fluid dynamics, optics, ultrasound propagation, thermal propagation, and automotive safety research. These and other applications provide ample justification for the realization of the Visible Human Project® (VHP)-Female v. 4.0, a new platform-independent full body electromagnetic computational model. Along with the VHP-Female v. 4.0, a realistic and anatomically justified Dynamic Breathing Sequence is developed. The creation of such model is essential to the development of biomedical devices and procedures that are affected by the dynamics of human breathing, such as Magnetic Resonance Imaging and the calculation of Specific Absorption Rate. The model can be used in numerous application, including Breath-Detection Radar for human search and rescue

Morphology of the inner structures of the facial skeleton in Homo neanderthalensis and the case-study of the Neanderthal from Altamura (Bari, Italy)

The PhD project has the aim to provide an accurate anatomical characterization of the facial regions (with a focus on the para-nasal areas) in the fossil human species Homo neanderthalensis, whose peculiar facial morphology is the topic of unresolved hypothesis on adaptation to climate and/or phylogenetic factors. Both can be at the origin of the variability of Neanderthals and can be taken into consideration, more in general, for the human populations from the Middle and Upper Pleistocene of Europe, thus from around 800 to 11 thousand years ago (ka). In this timespan, it can be seen a differential development of a set of cranial features which was resumed by J.J. Hublin and colleagues with the ‘accretion model’. In this scenario, a Neanderthal specimen from Italy, known as the ‘Altamura Man’ and discovered in 1993 in the Lamalunga karstic system in Apulia (southern Italy), represents a crucial subject of study, because its unique state of preservation and its antiquity, comprised between 172 and 130 ka. The nearly complete skeleton is still preserved in situ because of several factors, among which its exceptional completeness and thus has been the subject of a study of virtual paleoanthropology aimed at the reconstruction and observation of facial structures often damaged or completely absent in the fossil record

Development of Human Body CAD Models and Related Mesh Processing Algorithms with Applications in Bioelectromagnetics

Simulation of the electromagnetic response of the human body relies heavily upon efficient computational CAD models or phantoms. The Visible Human Project (VHP)-Female v. 3.1 - a new platform-independent full-body electromagnetic computational model is revealed. This is a part of a significant international initiative to develop powerful computational models representing the human body. This model’s unique feature is full compatibility both with MATLAB and specialized FEM computational software packages such as ANSYS HFSS/Maxwell 3D and CST MWS. Various mesh processing algorithms such as automatic intersection resolver, Boolean operation on meshes, etc. used for the development of the Visible Human Project (VHP)-Female are presented. The VHP - Female CAD Model is applied to two specific low frequency applications: Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS). TMS and tDCS are increasingly used as diagnostic and therapeutic tools for numerous neuropsychiatric disorders. The development of a CAD model based on an existing voxel model of a Japanese pregnant woman is also presented. TMS for treatment of depression is an appealing alternative to drugs which are teratogenic for pregnant women. This CAD model was used to study fetal wellbeing during induced peak currents by TMS in two possible scenarios: (i) pregnant woman as a patient; and (ii) pregnant woman as an operator. An insight into future work and potential areas of research such as a deformable phantom, implants, and RF applications will be presented

Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations

There is no denying how machine learning and computer vision have grown in the recent years. Their highest advantages lie within their automation, suitability, and ability to generate astounding results in a matter of seconds in a reproducible manner. This is aided by the ubiquitous advancements reached in the computing capabilities of current graphical processing units and the highly efficient implementation of such techniques. Hence, in this paper, we survey the key studies that are published between 2014 and 2020, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic-tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and further partitioned if the amount of works that fall under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites, containing masks of the aforementioned tissues, are thoroughly discussed, highlighting the organizers original contributions, and those of other researchers. Also, the metrics that are used excessively in literature are mentioned in our review stressing their relevancy to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing such as the scarcity of many studies on the vessels segmentation challenge, and why their absence needs to be dealt with in an accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver tissues segmentation based on automated ML-based technique