Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device

Hierarchical Structure, Properties and Bone Mechanics at Macro, Micro, and Nano Levels

This research focuses on the hierarchical structure of bone and associated mechanical properties at different scales to assess damage accumulation leading to premature failure, with or without instrumentation. In this work, an attempt was made to develop a framework of macro, micro, and nano damage accumulation models and implementing them to derive mechanical behavior of the bone. At macrolevel, retrospective evaluation of 313 subjects was conducted, and the damage of bone tissue was investigated with respect to subject demography including age, gender, race, body mass index (BMI), height and weight, and their role in initiating fracture. Experimental data utilized 28 human femoral bones implanted with cephalomedullary nails were used to develop damage prediction models. Investigation of three real life medical device failures identified the mechanical and clinical bases of bone failure. At the micro level, microdamage accumulation of the bone was investigated in 307 subjects and new effective morphological parameters at microscale were proposed. At the nano level, molecular dynamics simulation was performed to investigate the effect of interaction, orientation, and hydration on the atomic models of the bone composed of collagen helix and hydroxyapatite crystal. The results showed that bone density and maximum von Mises stress decreased drastically in elderly patients, implying fixation devices and implants used by the young cannot be used. The results also showed that the two-dimensional representation of the morphological parameters of the bone at microscale does not provide a realistic description of bone structure. Therefore, in this work, three-dimensional representations at microscale indicated that bone interconnectivity is higher in female patients than in male patients. Gender has a significant effect on microdamage distribution in the bone. More precautions should be taken into consideration for older female patients. Race should also be considered during modeling implants or suggesting therapeutic techniques. Caucasian subjects are more susceptible to bone fatigue failure than other races. The mechanical properties of bone are affected significantly by the orientation of the collagen fibril, which varies between ethnicities. Any change in the structure of the collagen-hydroxyapatite composite leads to variable bone diseases. There is significant difference in the ultimate tensile strength and toughness of the bone with respect to the orientation of the hydrated and un-hydrated collagen fibrils. Water content also influences the bone tissues’ elastic properties. The force in longitudinal direction (0°) provides more strength compared with the collagen fibril in the perpendicular direction (90°). Substituting Glycine with other amino acids affects the mechanical properties and strength of the collagen helix, collagen-hydroxyapatite interface, and eventually affecting hydroxyapatite crystal. Appropriate models developed in each category showing experimental and computational relationships and their application in selecting implant materials

Pathological and Biomedical Characteristics of Spinal Cord Injury Determined Using Diffusion Tensor Imaging

Traumatic spinal cord injury: SCI) is the most devastating injury that often causes the victim permanent paralysis and undergo a lifetime of therapy and care. It is caused by a mechanical impact that ultimately causes pathophysiological consequences which at this moment in time are an unresolved scientific challenge of great social impact. Scientists have long used animal contusion models to study the pathophysiology of SCI in the discovery of progressive secondary tissue degeneration, demyelination, and apoptosis. More importantly, most therapies that have gone to human clinical trial were first validated in spinal cord contusion models. Magnetic resonance imaging: MRI) is the modality of choice to noninvasively detect the soft tissue injury, particularly suitable for assessing the tissue integrity in SCI. However, the convention MRI lacks capability of detecting and evaluating the injury severity acutely, probably resulting in lost opportunities of effective prognostication or treatment stratification for SCI patients. Diffusion Tensor Magnetic Resonance Imaging: DTMRI, DTI) is an emerging technique known to provide dynamic contrast reflecting the progression of the underlying pathology in CNS tissues. In this study, we hypothesized that axial: ||) and radial: λ^) diffusivity derived from DTI is sensitive to the pathological alteration in spinal cord white matter: WM) tract and could be used as potential biomarkers detecting and characterizing the axonal and myelin damage in SCI. A mouse model of contusion SCI was examined using DTI, behavioral assessment, and histology to test our hypothesis. Techniques employed including the simplification of diffusion weighting scheme, the implementation of diffusion weighted multiple spin-echo sequence, and verified for setting up the experimental protocol and data processing procedures. Secondly, the hypothesis was test on the projects comparing the change of these biomarkers on both the myelinated and dysmyelinated shiverer mice cooperating with histological analysis, and behavioral assessment. Finally, a finite element analysis: FEA) of contusion SCI was deployed to provide evidences of injury mechanics correlated with the injury patterns detected by diffusion MRI for a better characterized animal model of contusion SCI

An MRI Segmentation Framework for Brains with Anatomical Deviations

The segmentation of brain Magnetic Resonance (MR) images, where the brain is partitioned into anatomical regions of interest, is a notoriously difficult problem when the underlying brain structures are influenced by pathology or are undergoing rapid development. This dissertation proposes a new automatic segmentation method for brain MRI that makes use of a model of a homogeneous population to detect anatomical deviations. The chosen population model is a brain atlas created by averaging a set of MR images and the corresponding segmentations. The segmentation method is an integration of robust parameter estimation techniques and the Expectation-Maximization algorithm. In clinical applications, the segmentation of brains with anatomical deviations from those commonly observed within a homogeneous population is of particular interest. One example is provided by brain tumors, since delineation of the tumor and of any surrounding edema is often critical for treatment planning. A second example is provided by the dynamic brain changes that occur in newborns, since study of these changes may generate insights into regional growth trajectories and maturation patterns. Brain tumor and edema can be considered as anatomical deviations from a healthy adult population, whereas the rapid growth of newborn brains can be considered as an anatomical deviation from a population of fully developed infant brains. A fundamental task associated with image segmentation is the validation of segmentation accuracy. In cases in which the brain deviates from standard anatomy, validation is often an ill-defined task since there is no knowledge of the ground truth (information about the actual structures observed through MRI). This dissertation presents a new method of simulating ground truth with pathology that facilitates objective validation of brain tumor segmentations. The simulation method generates realistic-appearing tumors within the MRI of a healthy subject. Since the location, shape, and volume of the synthetic tumors are known with certainty, the simulated MRI can be used to objectively evaluate the accuracy of any brain tumor segmentation method

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Tissue-scale, patient-specific modeling and simulation of prostate cancer growth

Programa Oficial de Doutoramento en Enxeñaría Civil . 5011V01[Abstract] Prostate cancer is a major health problem among aging men worldwide. This pathology is easier to cure in its early stages, when it is still organ-confined. However, it hardly ever produces any symptom until it becomes excessively large or has invaded other tissues. Hence, the current approach to combat prostate cancer is a combination of prevention and regular screening for early detection. Indeed, most cases of prostate cancer are diagnosed and treated when it is localized within the organ. Despite the wealth of accumulated knowledge on the biological basis and clinical management of the disease, we lack a comprehensive theoretical model into which we can organize and understand the abundance of data on prostate cancer. Additionally, the standard clinical practice in oncology is largely based on statistical patterns, which is not sufficiently accurate to individualize the diagnosis, prediction of prognosis, treatment, and follow-up. Recently, mathematical modeling and simulation of cancer and their treatments have enabled the prediction of clinical outcomes and the design of optimal therapies on a patient-specific basis. This new trend in medical research has been termed mathematical oncology. Prostate cancer is an ideal candidate to benefit from this technology for several reasons. First, patient-specific clinical approaches may contribute to reduce the rates of overtreatment and undertreatment of prostate cancer. Multiparametric magnetic resonance is increasingly used to monitor and diagnose this disease. This imaging technology can provide abundant information to build a patient-specific mathematical model of prostate cancer growth. Moreover, the prostate is a sufficiently small organ to pursue tissue-scale predictive simulations. Prostate cancer growth can also be estimated using the serum concentration of a biomarker known as the prostate specific antigen. Additionally, some prostate cancer patients do not receive any treatment but are clinically monitored and periodically imaged, which opens the door to in vivo model validation. The advent of versatile and powerful technologies in computational mechanics permits to address the challenges posed by the prostate anatomy and the resolution of the mathematical models. Finally, mathematical oncology technologies can guide the future research on prostate cancer, e.g., proposing new treatment strategies or unveiling mechanisms involved in tumor growth. Therefore, the aim of this thesis is to provide a computational framework for the tissuescale, patient-specific modeling and simulation of organ-confined PCa growth within the context of mathematical oncology. We present a model for localized prostate cancer growth that reproduces the growth patterns of the disease observed in experimental and clinical studies. To capture the coupled dynamics of healthy and tumoral tissue, we use the phase-field method together with reaction-diffusion equations for nutrient consumption and prostate specific antigen production. We leverage this model to run the first tissue-scale, patient-specific simulations of prostate cancer growth over the organ anatomy extracted from medical images. Our results show similar tumor progression as observed in clinical practice. We leverage isogeometric analysis to handle the nonlinearity of our set of equations, as well as the complex anatomy of the prostate and the intricate tumoral morphologies. We further advocate dynamical mesh adaptivity to speed up calculations, rationalize computational resources, and facilitate simulation in a clinically relevant time. We present a set of efficient algorithms to accommodate local h-refinement and h-coarsening of hierarchical splines in isogeometric analysis. Our methods are based on Bézier projection, which we extend to hierarchical spline spaces. We also introduce a balance parameter to control the overlapping of basis functions across the levels of the hierarchy, leading to improved numerical conditioning. Our simulations of cancer growth show remarkable accuracy with very few degrees of freedom in comparison to the uniform mesh that the same simulation would require. Finally, we study the interaction between prostate cancer and benign prostatic hyperplasia, another common prostate pathology that causes the organ to gradually enlarge. In particular, we investigate why tumors originating in larger prostates present favorable pathological features. We perform a qualitative simulation study by extending our mathematical model of prostate cancer growth to include the equations of mechanical equilibrium and the coupling terms between them and tumor dynamics. We assume that the deformation of the prostate is a quasistatic phenomenon and we model prostatic tissue as a linear elastic, heterogeneous, isotropic material. This model is calibrated by studying the deformation caused by either disease independently. Our simulations show that a history of benign prostatic hyperplasia creates mechanical stress fields in the prostate that hamper prostatic tumor growth and limit its invasiveness.[Resumen] El cáncer de próstata es un gran problema de salud en hombres de edad avanzada en todo el mundo. Esta patología es más fácil de curar en sus estadios iniciales, cuando aún es órgano-confinada. Sin embargo, casi nunca produce ningún síntoma hasta que es demasiado grande o ha invadido otros tejidos. Por tanto, el enfoque actual para combatir el cáncer de próstata es una combinación de prevención y exámenes rutinarios para una detección precoz. De hecho, la mayoría de casos de cáncer de próstata son diagnosticados y tratados cuando aún está localizado dentro del órgano. A pesar de la riqueza del conocimiento acumulado sobre las bases biológicas y la gestión clínica de la enfermedad, carecemos de un modelo teórico completo en el que podamos organizar y comprender la enorme cantidad de datos existentes sobre el cáncer de próstata. Además, la práctica clínica estándar en oncología está basada en gran medida en patrones estadísticos, lo cual no es suficientemente preciso para individualizar el diagnóstico, la predicción de la prognosis, el tratamiento y el seguimiento. Recientemente, la modelización y la simulación matemáticas del cáncer y sus tratamientos han permitido predecir resultados clínicos y el diseño de terapias óptimas de forma personalizada. Esta nueva corriente de investigación médica se ha denominado oncología matemática. El cáncer de próstata es un candidato ideal para beneficiarse de esta tecnología por varios motivos. En primer lugar, un enfoque clínico personalizado podría contribuir a reducir las tasas de tratamiento excesivo o insuficiente de cáncer de próstata. La resonancia magnética multiparamétrica se usa cada vez más para monitorizar y diagnosticar esta enfermedad. Esta tecnología de imagen puede proporcionar abundante información para construir un modelo matemático de crecimiento de cáncer de próstata personalizado. Además, la próstata es un órgano suficientemente pequeño para perseguir la realización de simulaciones predictivas a escala tisular. El crecimiento del cáncer de próstata también se puede estimar usando la concentración en sangre de un biomarcador conocido como el antígeno prostático específico. Adicionalmente, algunos pacientes de cáncer de próstata no reciben tratamiento pero son monitorizados clínicamente y se les toman imágenes médicas periódicamente, lo que abre la puerta a la validación in vivo de modelos. El desarrollo de tecnologías versátiles y potentes en mecánica computacional permite hacer frente a los retos derivados de la anatomía prostática y la resolución de los modelos matemáticos. Finalmente, las tecnologías de oncología matemática pueden guiar las investigaciones futuras sobre cáncer de próstata, por ejemplo, proponiendo nuevas estrategias de tratamiento o descubriendo mecanismos involucrados en el crecimiento tumoral. Por tanto, el objeto de esta tesis es proporcionar un marco computacional para la modelización y simulación del crecimiento del cáncer de próstata órgano-confinado de forma personalizada y a escala tisular dentro del contexto de la oncología matemática. Presentamos un modelo de crecimiento de cáncer de próstata localizado que reproduce los patrones de crecimiento de la enfermedad observados en estudios experimentales y clínicos. Para capturar las dinámicas acopladas de los tejidos sano y tumoral, usamos el método de campo de fase junto con ecuaciones de reacción-difusión para el consumo de nutriente y la producción de antígeno prostático específico. Empleamos este modelo para realizar las primeras simulaciones personalizadas a escala tisular del crecimiento de cáncer de próstata sobre la anatomía del órgano extraída de imágenes médicas. Nuestros resultados muestran una progresión tumoral similar a la observada en la práctica clínica. Utilizamos el análisis isogeométrico para resolver la no-linealidad de nuestro sistema de ecuaciones, así como la compleja anatomía de la próstata y las intricadas morfologías tumorales. Adicionalmente, proponemos el uso de adaptatividad dinámica de malla para acelerar los cálculos, racionalizar los recursos computacionales y facilitar la simulación en un tiempo clínicamente relevante. Presentamos un conjunto de algoritmos eficientes para introducir el refinamiento y el engrosado locales tipo h en análisis isogeométrico. Nuestros métodos están basados en la proyección de Bézier, que extendemos a los espacios de splines jerárquicas. También introducimos un parámetro de balance para controlar la superposición de funciones de base a través de los niveles de la jerarquía, lo cual conduce a un condicionamiento numérico mejorado. Nuestras simulaciones de crecimiento de cáncer muestran una notable precisión con muy pocos grados de libertad en comparación con la malla uniforme que la misma simulación requeriría. Finalmente, estudiamos la interacción entre el cáncer de próstata y la hiperplasia benigna de próstata, otra patología prostática común que hace crecer al órgano gradualmente. En particular, investigamos por qué los tumores que se originan en próstatas más grandes presentan características patológicas favorables. Realizamos un estudio de simulación cualitativo extendiendo nuestro modelo matemático de crecimiento de cáncer de próstata para incluir las ecuaciones de equilibrio mecánico y los términos de acoplamiento entre estas y la dinámica tumoral. Asumimos que la deformación de la próstata es un fenómeno cuasiestático y modelamos el tejido prostático como un material elástico lineal, heterogéneo e isotrópico. Este modelo es calibrado estudiando la deformación causada por cada enfermedad independientemente. Nuestras simulaciones muestran que un historial de hiperplasia benigna de próstata crea campos de tensión mecánica en la próstata que obstaculizan el crecimiento del cáncer de próstata y limitan su invasividad.[Resumo] O cancro de próstata é un gran problema de saúde en homes de idade avanzada en todo o mundo. Esta patoloxía é máis fácil de curar nos seus estadios iniciais, cando aínda é órgano-confinada. Porén, case nunca produce ningún síntoma ata que é demasiado grande ou ten invadido outros tecidos. Polo tanto, o enfoque actual para combater o cancro de próstata é unha combinación de prevención e exames rutinarios para unha detección precoz. De feito, a maioría de casos de cancro de próstata son diagnosticados e tratados cando aínda está localizado dentro do órgano. Malia a riqueza do coñecemento acumulado sobre as bases biolóxicas e a xestión clínica da doenza, carecemos dun modelo teórico completo no que podamos organizar e comprender a enorme cantidade de datos existentes sobre o cancro de próstata. Ademais, a práctica clínica estándar en oncoloxía está baseada en gran medida en patróns estatísticos, o cal non é suficientemente preciso para individualizar a diagnose, a predición da prognose, o tratamento e o seguimento. Recentemente, a modelización e a simulación matemáticas do cancro e os seus tratamentos permitiron predicir resultados clínicos e o deseño de terapias óptimas de forma personalizada. Esta nova corrente de investigación médica denomínase oncoloxía matemática. O cancro de próstata é un candidato ideal para beneficiarse desta tecnoloxía por varios motivos. En primeiro lugar, un enfoque clínico personalizado podería contribuír a reducir as taxas de tratamento excesivo ou insuficiente de cancro de próstata. A resonancia magnética multiparamétrica úsase cada vez máis para monitorizar e diagnosticar esta enfermidade. Esta tecnoloxía de imaxe pode proporcionar abundante información para construír un modelo matemático de crecemento de cancro de próstata personalizado. Ademais, a próstata é un órgano suficientemente pequeno para perseguir a realización de simulacións preditivas a escala tisular. O crecemento do cancro de próstata tamén se pode estimar usando a concentración en sangue dun biomarcador coñecido como o antíxeno prostático específico. Adicionalmente, algúns pacientes de cancro de próstata non reciben tratamento pero son monitorizados clinicamente e se lles toman imaxes médicas periodicamente, o que abre a porta á validación in vivo de modelos. O desenvolvemento de tecnoloxías versátiles e potentes en mecánica computacional permite facer fronte aos retos derivados da anatomía prostática e a resolución dos modelos matemáticos. Finalmente, as tecnoloxías de oncoloxía matemática poden guiar as investigacións futuras sobre cancro de próstata, por exemplo, propoñendo novas estratexias de tratamento ou descubrindo mecanismos involucrados no crecemento tumoral. Polo tanto, o obxecto desta tese é proporcionar un marco computacional para a modelización e simulación do crecemento do cancro de próstata órgano-confinado de forma personalizada e a escala tisular dentro do contexto da oncoloxía matemática. Presentamos un modelo de crecemento de cancro de próstata localizado que reproduce os patróns de crecemento da enfermidade observados en estudos experimentais e clínicos. Para capturar as dinámicas acopladas dos tecidos san e tumoral, usamos o método de campo de fase xunto con ecuacións de reacción-difusión para o consumo de nutriente e a produción de antíxeno prostático específico. Empregamos este modelo para realizar as primeiras simulacións personalizadas a escala tisular do crecemento de cancro de próstata sobre a anatomía do órgano extraída de imaxes médicas. Os nosos resultados amosan unha progresión tumoral similar á observada na práctica clínica. Utilizamos a análise isoxeométrica para resolver a non-linealidade do noso sistema de ecuacións, así como a complexa anatomía da próstata e as intricadas morfoloxías tumorais. Adicionalmente, propoñemos o uso de adaptatividade dinámica de malla para acelerar os cálculos, racionalizar os recursos computacionais e facilitar a simulación nun tempo clinicamente relevante. Presentamos un conxunto de algoritmos eficientes para introducir o refinamento e o engrosado locais tipo h en análise isoxeométrica. Os nosos métodos están baseados na proxección de Bézier, que estendemos aos espazos de splines xerárquicas. Tamén introducimos un parámetro de balance para controlar a superposición de funcións de base a través dos niveis da xerarquía, o cal conduce a un condicionamento numérico mellorado. As nosas simulacións de crecemento de cancro amosan unha notable precisión con moi poucos graos de liberdade en comparación coa malla uniforme que a mesma simulación requiriría. Finalmente, estudamos a interacción entre o cancro de próstata e a hiperplasia benigna de próstata, outra patoloxía prostática común que fai crecer ao órgano gradualmente. En particular, investigamos por que os tumores que se orixinan en próstatas máis grandes presentan características patolóxicas favorables. Realizamos un estudo de simulación cualitativo estendendo o noso modelo matemático de crecemento de cancro de próstata para incluír as ecuacións de equilibrio mecánico e os termos de acoplamento entre estas e a dinámica tumoral. Asumimos que a deformación da próstata é un fenómeno cuasiestático e modelamos o tecido prostático como un material elástico lineal, heteroxéneo e isotrópico. Este modelo é calibrado estudando a deformación causada por cada enfermidade independientemente. As nosas simulacións amosan que un historial de hiperplasia benigna de próstata crea campos de tensión mecánica na próstata que obstaculizan o crecemento do cancro de próstata e limitan a súa invasividade

Failure Mechanics of Nonlinear, Heterogeneous, Anisotropic Cardiovascular Tissues: Implications for Ascending Thoracic Aortic Aneurysms

University of Minnesota Ph.D. dissertation. June 2019. Major: Biomedical Engineering. Advisor: Victor Barocas. 1 computer file (PDF); xi, 235 pages.Characterizing the mechanical response and failure mechanisms of cardiovascular tissues is critically important, as these tissues play a vital role in the native functioning of the body. In the case of pathological events, such as aortic aneurysms or myocardial infarctions, mechanical behavior can be altered due to adverse remodeling, and thus affect the integrity of the tissue. Ascending thoracic aortic aneurysms (ATAAs) occur when the aorta enlarges beyond its normal diameter, and dilation is typically accompanied by disorganization of the underlying aortic fibrous structure. Current diagnostic methods depend solely on measuring aneurysm diameter, neglecting considerations of mechanical strength, which results in an inefficient risk assessment. To better understand the failure mechanism of ATAAs, the work presented here used a combination of experimental testing and computational modeling to characterize failure in human ATAA tissue. Experimental testing showed that ATAA tissue exhibited significantly lower mechanical strength when compared to healthy porcine tissue in multiple loading configurations. Furthermore, experimental tests highlighted the large disparity between uniaxial and shear strength in ATAA tissue, where the tissue was substantially weaker in shear loading conditions. A custom multiscale finite-element model was then used to interrogate fiber failure more closely in both experimental loading conditions, and inflation of a patient-specific ATAA geometry. Modeling results showed that fibers between the lamellar layers of the aortic wall failed significantly more than fibers within the planar layers in shear loading conditions, as well as during inflation of the patient-specific geometry. Taken together, these results suggest that intramural shear could be an important contributor to the failure or dissection of ATAAs

Image analytic tools for tissue characterization using optical coherence tomography

Optical coherence tomography (OCT) has been emerging as a promising imaging technique, with a strong capability of non-invasive, in vivo, high resolution, depth-resolved imaging. There is a great potential to use OCT to guide the treatment of arrhythmias, to prevent preterm birth, and to detect breast cancer. To facilitate the clinical applications, this thesis presents three image analytic tools to characterize biological tissue: 1) automated fiber direction analysis; 2) automated volumetric stitching; 3) automated tissue classification. The fiber direction analysis consists of a particle-filter-based 3D tractography scheme and a pixel-wise fiber analysis scheme. The stitching algorithm enlarges the field of view of current OCT system from millimeter to centimeter level by volumetric stitching using scale-invariant feature transform. Based on relevance vector machine, a region-based classification scheme and a grid-based classification scheme are developed to automatically identify tissue composition in human cardiac tissue and human breast tissue. These tools are collaboratively used to study OCT images from cardiac, cervical, and breast tissue. In cardiac tissue, we apply the fiber orientation analysis to reconstruct 3D cardiac myofibers tractography and perform pixel-wise fiber analysis on the collagen region within human heart. In addition, we apply the region-based algorithm to segment and classify tissue compositions, such as collagen, adipose tissue, fibrotic myocardium, and normal myocardium, over a single or a stitched OCT volume. Using our algorithm, we observe fiber directionality change over depths and find that the fiber orientation changes more dramatically in atria than in ventricle. We also observe different dispersion patterns within collagen layer. In cervical tissue, our stitching algorithm enables a paramount 3D view of entire axial slices. Together with pixel-wise fiber orientation scheme, we analyze the difference of dispersion property within inner/outer regions of four quadrants. We observe two dispersion patterns in pregnant and non-pregnant cervical tissue at the location close to upper cervix. In addition, we discover that an increasing trend of dispersion and an increasing trend of penetration depth from internal orifice (os) to external os. In breast tissue, we visualize various features in both benign and malignant tissues such as invasive ductal carcinoma (IDC), ductal carcinoma in situ, cyst, and terminal duct lobule unit in stitched OCT images. Focusing on the automated detection of IDC, we propose a hierarchy framework of classification model and apply our classifier in two OCT systems and achieve both reasonable sensitivity and specificity in identifying cancerous region