A non-invasive image based system for early diagnosis of prostate cancer.

Prostate cancer is the second most fatal cancer experienced by American males. The average American male has a 16.15% chance of developing prostate cancer, which is 8.38% higher than lung cancer, the second most likely cancer. The current in-vitro techniques that are based on analyzing a patients blood and urine have several limitations concerning their accuracy. In addition, the prostate Specific Antigen (PSA) blood-based test, has a high chance of false positive diagnosis, ranging from 28%-58%. Yet, biopsy remains the gold standard for the assessment of prostate cancer, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The major limitation of the relatively small needle biopsy samples is the higher possibility of producing false positive diagnosis. Moreover, the visual inspection system (e.g., Gleason grading system) is not quantitative technique and different observers may classify a sample differently, leading to discrepancies in the diagnosis. As reported in the literature that the early detection of prostate cancer is a crucial step for decreasing prostate cancer related deaths. Thus, there is an urgent need for developing objective, non-invasive image based technology for early detection of prostate cancer. The objective of this dissertation is to develop a computer vision methodology, later translated into a clinically usable software tool, which can improve sensitivity and specificity of early prostate cancer diagnosis based on the well-known hypothesis that malignant tumors are will connected with the blood vessels than the benign tumors. Therefore, using either Diffusion Weighted Magnetic Resonance imaging (DW-MRI) or Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), we will be able to interrelate the amount of blood in the detected prostate tumors by estimating either the Apparent Diffusion Coefficient (ADC) in the prostate with the malignancy of the prostate tumor or perfusion parameters. We intend to validate this hypothesis by demonstrating that automatic segmentation of the prostate from either DW-MRI or DCE-MRI after handling its local motion, provides discriminatory features for early prostate cancer diagnosis. The proposed CAD system consists of three majors components, the first two of which constitute new research contributions to a challenging computer vision problem. The three main components are: (1) A novel Shape-based segmentation approach to segment the prostate from either low contrast DW-MRI or DCE-MRI data; (2) A novel iso-contours-based non-rigid registration approach to ensure that we have voxel-on-voxel matches of all data which may be more difficult due to gross patient motion, transmitted respiratory effects, and intrinsic and transmitted pulsatile effects; and (3) Probabilistic models for the estimated diffusion and perfusion features for both malignant and benign tumors. Our results showed a 98% classification accuracy using Leave-One-Subject-Out (LOSO) approach based on the estimated ADC for 30 patients (12 patients diagnosed as malignant; 18 diagnosed as benign). These results show the promise of the proposed image-based diagnostic technique as a supplement to current technologies for diagnosing prostate cancer

Imaging in pulmonary hypertension: the role of MR and CT

Pulmonary hypertension (PH) is a debilitating disease with many causes that has a significant impact on quality of life and results in premature death. Until recently imaging has only played an adjunctive role to primary diagnostic modalities such as echocardiography and right heart catheterization in identifying these patients. The advent of newer imaging techniques and developments in hardware has opened up a new scope for imaging. CT offers excellent structural detail while MRI provides superb functional information without the risk of radiation. These modalities now offer a robust and in-depth diagnostic approach for the investigation of patients with suspected pulmonary hypertension. This document explores the role of MR and CT imaging methods in investigating patients with pulmonary vascular disease and different aspect of lung disease. In particular, subgroups of pulmonary hypertension associated with unique morphological changes have been closely scrutinized. In this work the value of MR angiography in patients suspected with chronic thromboembolic pulmonary hypertension or unexplained PH has been explored and in the same subgroup of patients, the role of 3D MR lung perfusion as a diagnostic tool has also been demonstrated. This research has also shown that the thoracic CT offers valuable prognostic information and imaging characteristics in patients with each of the major subcategories of pulmonary arterial hypertension. Furthermore, the diagnostic accuracy and prognostic significance of MR and CT indices for the detection of PH in patients with connective tissue disease associated with PH has been highlighted. Finally, the feasibility and diagnostic quality of MRI to identify structural parenchymal lung changes have also been analysed and this study demonstrates the potential clinical utility of imaging high risk patients with MRI in longitudinal studies thereby avoiding the hazards of radiation exposure

Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation

Image segmentation is a fundamental and challenging problem in computer vision with applications spanning multiple areas, such as medical imaging, remote sensing, and autonomous vehicles. Recently, convolutional neural networks (CNNs) have gained traction in the design of automated segmentation pipelines. Although CNN-based models are adept at learning abstract features from raw image data, their performance is dependent on the availability and size of suitable training datasets. Additionally, these models are often unable to capture the details of object boundaries and generalize poorly to unseen classes. In this thesis, we devise novel methodologies that address these issues and establish robust representation learning frameworks for fully-automatic semantic segmentation in medical imaging and mainstream computer vision. In particular, our contributions include (1) state-of-the-art 2D and 3D image segmentation networks for computer vision and medical image analysis, (2) an end-to-end trainable image segmentation framework that unifies CNNs and active contour models with learnable parameters for fast and robust object delineation, (3) a novel approach for disentangling edge and texture processing in segmentation networks, and (4) a novel few-shot learning model in both supervised settings and semi-supervised settings where synergies between latent and image spaces are leveraged to learn to segment images given limited training data.Comment: PhD dissertation, UCLA, 202

Quantification of Pulmonary Ventilation using Hyperpolarized 3He Magnetic Resonance Imaging

Smoking-related lung diseases including chronic obstructive pulmonary disease (COPD) and lung cancer are projected to have claimed the lives of more than 30,000 Canadians in 2010. The poor prognosis and lack of new treatment options for lung diseases associated with smoking are largely due to the inadequacy of current techniques for evaluating lung function. Hyperpolarized 3He magnetic resonance imaging (MRI) is a relatively new technique, and quantitative measurements derived from these images, specifically the ventilation defect volume (VDV) and ventilation defect percent (VDP) have the potential to provide new sensitive measures of lung function. Here, we evaluate the reproducibility of VDV, and explore the sensitivity of these measurements in healthy young and elderly volunteers, and subjects with smoking-related lung disease (COPD and radiation-induced lung injury (RILI)). Our results show that 3He MRI measurements of ventilation have high short-term reproducibility in both healthy volunteers and subjects with COPD. Additionally, we report that these measurements are sensitive to age-related changes in lung function. Finally, in RILI we show that measurements of lung function derived from 3He MRI are sensitive to longitudinal changes in lung function following treatment, while in COPD we report that using VDP in conjunction with structural measurements of disease (using the apparent diffusion coefficient (ADC) derived from diffusion-weighted images) may provide a new method for phenotyping this smoking-related lung disease