Multiscale mechano-morphology of soft tissues : a computational study with applications to cancer diagnosis and treatment

Cooperation of engineering and biomedical sciences has produced significant advances in healthcare technology. In particular, computational modelling has led to a faster development and improvement of diagnostic and treatment techniques since it allows exploring multiple scenarios without additional complexity and cost associated to the traditional trial-and-error methodologies. The goal of this thesis is to propose computational methodologies to analyse how the changes in the microstructure of soft tissues, caused by different pathological conditions, influence the mechanical properties at higher length scales and, more importantly, to detect such changes for the purpose of quantitative diagnosis and treatment of such pathologies in the scenario of drug delivery. To achieve this objective different techniques based on quasi-static and dynamic probing have been established to perform quantitative tissue diagnosis at the microscopic (tissue) and macroscopic (organ) scales. The effects of pathologies not only affect the mechanical properties of tissue (e.g. elasticity and viscoelasticity), but also the transport properties (e.g. diffusivity) in the case of drug delivery. Such transport properties are further considered for a novel multi-scale, patient-specific framework to predict the efficacy of chemotherapy in soft tissues. It is hoped that this work will pave the road towards non-invasive palpation techniques for early diagnosis and optimised drug delivery strategies to improve the life quality of patientsJames-Watt Scholarship, Heriot-Watt Annual Fund and the Institute of Mechanical, Process and Energy Engineering (IMPEE) Grant

Proceedings, MSVSCC 2017

Proceedings of the 11th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 20, 2017 at VMASC in Suffolk, Virginia. 211 pp

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields

Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach

In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications in medical image and shape analysis are investigated. In the first contribution, a manifold learning-based multi-modal image registration technique is developed, which results in a unified intensity system through intensity transformation between the reference and sensed images. The transformation eliminates intensity variations in multi-modal medical scans and hence facilitates employing well-studied mono-modal registration techniques. The method can be used for registering multi-modal images with full and partial data. Next, a manifold learning-based scale invariant global shape descriptor is introduced. The proposed descriptor benefits from the capability of Laplacian Eigenmap in dealing with high dimensional data by introducing an exponential weighting scheme. It eliminates the limitations tied to the well-known cotangent weighting scheme, namely dependency on triangular mesh representation and high intra-class quality of 3D models. In the end, a novel descriptive model for diagnostic classification of pulmonary nodules is presented. The descriptive model benefits from structural differences between benign and malignant nodules for automatic and accurate prediction of a candidate nodule. It extracts concise and discriminative features automatically from the 3D surface structure of a nodule using spectral features studied in the previous work combined with a point cloud-based deep learning network. Extensive experiments have been conducted and have shown that the proposed algorithms based on manifold learning outperform several state-of-the-art methods. Advanced computational techniques with a combination of manifold learning and deep networks can play a vital role in effective healthcare delivery by providing a framework for several fundamental tasks in image and shape processing, namely, registration, classification, and detection of features of interest

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields

Cell Nuclear Morphology Analysis Using 3D Shape Modeling, Machine Learning and Visual Analytics

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with cell differentiation, development, proliferation, and disease. Changes in the nuclear form are associated with reorganization of chromatin architecture related to altered functional properties such as gene regulation and expression. Understanding these processes through quantitative analysis of morphological changes is important not only for investigating nuclear organization, but also has clinical implications, for example, in detection and treatment of pathological conditions such as cancer. While efforts have been made to characterize nuclear shapes in two or pseudo-three dimensions, several studies have demonstrated that three dimensional (3D) representations provide better nuclear shape description, in part due to the high variability of nuclear morphologies. 3D shape descriptors that permit robust morphological analysis and facilitate human interpretation are still under active investigation. A few methods have been proposed to classify nuclear morphologies in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. There is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analyses. In this work, we address a number of these existing limitations. First, we present a largest publicly available, to-date, 3D microscopy imaging dataset for cell nuclear morphology analysis and classification. We provide a detailed description of the image analysis protocol, from segmentation to baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. We proposed a specific cross-validation scheme that accounts for possible batch effects in data. Second, we propose a new technique that combines mathematical modeling, machine learning, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. Employing robust and smooth surface reconstruction methods to accurately approximate 3D object boundary enables the establishment of homologies between different biological shapes. Then, we compute geometric morphological measures characterizing the form of cell nuclei and nucleoli. We combine these methods into a highly parallel computational pipeline workflow for automated morphological analysis of thousands of nuclei and nucleoli in 3D. We also describe the use of visual analytics and deep learning techniques for the analysis of nuclear morphology data. Third, we evaluate proposed methods for 3D surface morphometric analysis of our data. We improved the performance of morphological classification between epithelial vs mesenchymal human prostate cancer cells compared to the previously reported results due to the more accurate shape representation and the use of combined nuclear and nucleolar morphometry. We confirmed previously reported relevant morphological characteristics, and also reported new features that can provide insight in the underlying biological mechanisms of pathology of prostate cancer. We also assessed nuclear morphology changes associated with chromatin remodeling in drug-induced cellular reprogramming. We computed temporal trajectories reflecting morphological differences in astroglial cell sub-populations administered with 2 different treatments vs controls. We described specific changes in nuclear morphology that are characteristic of chromatin re-organization under each treatment, which previously has been only tentatively hypothesized in literature. Our approach demonstrated high classification performance on each of 3 different cell lines and reported the most salient morphometric characteristics. We conclude with the discussion of the potential impact of method development in nuclear morphology analysis on clinical decision-making and fundamental investigation of 3D nuclear architecture. We consider some open problems and future trends in this field.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147598/1/akalinin_1.pd

Compact representations for fast nonrigid registration of medical images

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 167-179).We develop efficient techniques for the non-rigid registration of medical images by using representations that adapt to the anatomy found in such images. Images of anatomical structures typically have uniform intensity interiors and smooth boundaries. We create methods to represent such regions compactly using tetrahedra. Unlike voxel-based representations, tetrahedra can accurately describe the expected smooth surfaces of medical objects. Furthermore, the interior of such objects can be represented using a small number of tetrahedra. Rather than describing a medical object using tens of thousands of voxels, our representations generally contain only a few thousand elements. Tetrahedra facilitate the creation of efficient non-rigid registration algorithms based on finite element methods (FEM). We create a fast, FEM-based method to non-rigidly register segmented anatomical structures from two subjects. Using our compact tetrahedral representations, this method generally requires less than one minute of processing time on a desktop PC. We also create a novel method for the non-rigid registration of gray scale images. To facilitate a fast method, we create a tetrahedral representation of a displacement field that automatically adapts to both the anatomy in an image and to the displacement field. The resulting algorithm has a computational cost that is dominated by the number of nodes in the mesh (about 10,000), rather than the number of voxels in an image (nearly 10,000,000). For many non-rigid registration problems, we can find a transformation from one image to another in five minutes. This speed is important as it allows use of the algorithm during surgery.(cont.) We apply our algorithms to find correlations between the shape of anatomical structures and the presence of schizophrenia. We show that a study based on our representations outperforms studies based on other representations. We also use the results of our non-rigid registration algorithm as the basis of a segmentation algorithm. That algorithm also outperforms other methods in our tests, producing smoother segmentations and more accurately reproducing manual segmentations.by Samson J. Timoner.Ph.D

Approximating Computational Fluid Dynamics for Generative Design

Wind loads are a critical consideration in the early-stage design of tall buildings for mitigation of wind-induced forces through form modification. Existing research in computational fluid dynamics (CFD) development tends either towards fast-inaccurate or slow-accurate approaches; therefore offering either constrictive response time or inadequate accuracy. Novel approaches that combine both speed and accuracy are required to keep pace with developments in parametric design softwares, such as GenerativeComponents. These software tools, primarily used in early-stage generative design, allow for broad exploration and optimisation within the potential design space, which in turn requires commensurate fast-yet-accurate analysis tools. This thesis investigates the use of reduced-order models to approximate CFD simulations of wind pressure on tall buildings. It is hypothesised that: firstly, wind-induced surface pressure on tall buildings simulated by CFD can be locally approximated by geometric features; and secondly, reduced-order model predictions dominate CFD simulations in both time or accuracy and are therefore a novel non-dominated approach. Predictions are made of individual vertex pressure based on input features formed from local shape analysis. The vertex samples originate from a procedural model set which is evaluated with either steady-state Reynolds-averaged Navier-Stokes (RANS) or transient large eddy simulation (LES). An artificial neural network is used for model reduction with the training set of vertex samples; the basis methodology of which is tested on a range of study complexities. To prove the scalability of the approach, this culminates in the use of LES as the basis simulation, a test set of realistically complex building models, and an alternative approach to urban wind interference generalisation is also described, whereby a one-off large-scale context CFD simulation can be used as input to repeatable design model predictions. Furthermore, a prototype tool and an outline for its integration with an existing online analysis framework currently under development is presented. The quantitative and qualitative results of the studies show it is possible to approximate surface pressure from local shape features, thereby decoupling the prediction from the basis simulation. The reduced-order model can achieve fast-yet-accurate results, since prediction accuracy and time are invariant, or independent, of basis simulation accuracy and time; being instead solely a function of the reduced-order model performance and the geometric complexity or number of test mesh vertices. Evidence is demonstrated by the positioning of the results as a non-dominated solution in the time-accuracy objective space and the subsequent alteration of the existing Pareto frontier