Scalable Machine Learning Methods for Massive Biomedical Data Analysis.

Modern data acquisition techniques have enabled biomedical researchers to collect and analyze datasets of substantial size and complexity. The massive size of these datasets allows us to comprehensively study the biological system of interest at an unprecedented level of detail, which may lead to the discovery of clinically relevant biomarkers. Nonetheless, the dimensionality of these datasets presents critical computational and statistical challenges, as traditional statistical methods break down when the number of predictors dominates the number of observations, a setting frequently encountered in biomedical data analysis. This difficulty is compounded by the fact that biological data tend to be noisy and often possess complex correlation patterns among the predictors. The central goal of this dissertation is to develop a computationally tractable machine learning framework that allows us to extract scientifically meaningful information from these massive and highly complex biomedical datasets. We motivate the scope of our study by considering two important problems with clinical relevance: (1) uncertainty analysis for biomedical image registration, and (2) psychiatric disease prediction based on functional connectomes, which are high dimensional correlation maps generated from resting state functional MRI.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111354/1/takanori_1.pd

Quantitative lung CT analysis for the study and diagnosis of Chronic Obstructive Pulmonary Disease

The importance of medical imaging in the research of Chronic Obstructive Pulmonary Dis- ease (COPD) has risen over the last decades. COPD affects the pulmonary system through two competing mechanisms; emphysema and small airways disease. The relative contribu- tion of each component varies widely across patients whilst they can also evolve regionally in the lung. Patients can also be susceptible to exacerbations, which can dramatically ac- celerate lung function decline. Diagnosis of COPD is based on lung function tests, which measure airflow limitation. There is a growing consensus that this is inadequate in view of the complexities of COPD. Computed Tomography (CT) facilitates direct quantification of the pathological changes that lead to airflow limitation and can add to our understanding of the disease progression of COPD. There is a need to better capture lung pathophysiology whilst understanding regional aspects of disease progression. This has motivated the work presented in this thesis. Two novel methods are proposed to quantify the severity of COPD from CT by analysing the global distribution of features sampled locally in the lung. They can be exploited in the classification of lung CT images or to uncover potential trajectories of disease progression. A novel lobe segmentation algorithm is presented that is based on a probabilistic segmen- tation of the fissures whilst also constructing a groupwise fissure prior. In combination with the local sampling methods, a pipeline of analysis was developed that permits a re- gional analysis of lung disease. This was applied to study exacerbation susceptible COPD. Lastly, the applicability of performing disease progression modelling to study COPD has been shown. Two main subgroups of COPD were found, which are consistent with current clinical knowledge of COPD subtypes. This research may facilitate precise phenotypic characterisation of COPD from CT, which will increase our understanding of its natural history and associated heterogeneities. This will be instrumental in the precision medicine of COPD

Extended Quantitative Computed Tomography Analysis of Lung Structure and Function

Computed tomography (CT) imaging and quantitative CT (QCT) analysis for the study of lung health and disease have been rapidly advanced during the past decades, along with the employment of CT-based computational fluid dynamics (CFD) and machine learning approaches. The work presented in this thesis was devoted to extending the QCT analysis framework from three different perspectives.First, to extend the advanced QCT analysis to more data with undesirably protocolized CT scans, we developed a new deep learning-based automated segmentation of pulmonary lobes, in- corporating z-axis information into the conventional UNet segmentation. The proposed deep learn- ing segmentation, named ZUNet, was successfully applied for QCT analysis of silicosis patients with thick (5 or 10 mm) slices, which used to be excluded in QCT analysis since three-dimensional (3D) volumetric segmentation of the lungs and lobes were hardly successful or not automated. ZUNet outperformed UNet in lobe segmentation of human lungs. In addition, we extended the application of the QCT framework, combining CFD simulations for the entire subjects of the QCT analysis. One-dimensional (1D) CFD simulations of tidal breath- ing have been added to the inspiratory-expiratory CT image matching analysis of 66 asthma pa- tients (M:F=23:43, age=64.4±10.7) for pre- and post-bronchodilator comparison. We aimed to characterize comprehensive airway and lung structure and function relationship in the entire group response and patient-specific response to the bronchodilator. Along with the evidence of large air- way dilatation in the entire asthmatics, the CFD analysis revealed that improvements in regional flow rate fraction, particularly in the right lower lobe (RLL), airway pressure drop, airway resis- tance, and workload of breathing were significantly associated with the degree of large airway dilatation. Finally, we extended the approach using machine learning analysis to integrate numerous QCT variables with clinical features and additional information such as environmental exposure. In pursuit of investigating the effects of particulate matter (PM) exposure on human lung struc- ture and function alteration, principal component analysis (PCA) and k-means clustering iden- tified low, mid, and high exposure groups from directly measured air pollution exposure data of 270 healthy (age=68±10, M:F=15:51), asthma (age=60±12, M:F=39:56), chronic obstructive pulmonary disease (COPD) (age=69±7, M:F=66:10), and idiopathic pulmonary fibrosis (IPF) (age=72±7, M:F=43:10) subjects. Based on the exposure clusters, the RLL segmental airway narrowing was observed in the high exposure group. Various associations were found between the exposure data and about 200 multiscale lung features, from quantitative inspiratory and ex- piratory CT image matching and 1D CFD tidal breathing simulations. To highlight, small PM increases small airway disease in asthma. PM at all sizes decreases inspiratory low attenuation area in COPD and diseases luminal diameter of the RLL segmental airways in IPF

Shape analysis of the human brain.

Autism is a complex developmental disability that has dramatically increased in prevalence, having a decisive impact on the health and behavior of children. Methods used to detect and recommend therapies have been much debated in the medical community because of the subjective nature of diagnosing autism. In order to provide an alternative method for understanding autism, the current work has developed a 3-dimensional state-of-the-art shape based analysis of the human brain to aid in creating more accurate diagnostic assessments and guided risk analyses for individuals with neurological conditions, such as autism. Methods: The aim of this work was to assess whether the shape of the human brain can be used as a reliable source of information for determining whether an individual will be diagnosed with autism. The study was conducted using multi-center databases of magnetic resonance images of the human brain. The subjects in the databases were analyzed using a series of algorithms consisting of bias correction, skull stripping, multi-label brain segmentation, 3-dimensional mesh construction, spherical harmonic decomposition, registration, and classification. The software algorithms were developed as an original contribution of this dissertation in collaboration with the BioImaging Laboratory at the University of Louisville Speed School of Engineering. The classification of each subject was used to construct diagnoses and therapeutic risk assessments for each patient. Results: A reliable metric for making neurological diagnoses and constructing therapeutic risk assessment for individuals has been identified. The metric was explored in populations of individuals having autism spectrum disorders, dyslexia, Alzheimers disease, and lung cancer. Conclusion: Currently, the clinical applicability and benefits of the proposed software approach are being discussed by the broader community of doctors, therapists, and parents for use in improving current methods by which autism spectrum disorders are diagnosed and understood

Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to build specific systems to detect every possible condition. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For development, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system generalizes to new patient populations and abnormalities. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist

DEVELOPING NOVEL COMPUTER-AIDED DETECTION AND DIAGNOSIS SYSTEMS OF MEDICAL IMAGES

Reading medical images to detect and diagnose diseases is often difficult and has large inter-reader variability. To address this issue, developing computer-aided detection and diagnosis (CAD) schemes or systems of medical images has attracted broad research interest in the last several decades. Despite great effort and significant progress in previous studies, only limited CAD schemes have been used in clinical practice. Thus, developing new CAD schemes is still a hot research topic in medical imaging informatics field. In this dissertation, I investigate the feasibility of developing several new innovative CAD schemes for different application purposes. First, to predict breast tumor response to neoadjuvant chemotherapy and reduce unnecessary aggressive surgery, I developed two CAD schemes of breast magnetic resonance imaging (MRI) to generate quantitative image markers based on quantitative analysis of global kinetic features. Using the image marker computed from breast MRI acquired pre-chemotherapy, CAD scheme enables to predict radiographic complete response (CR) of breast tumors to neoadjuvant chemotherapy, while using the imaging marker based on the fusion of kinetic and texture features extracted from breast MRI performed after neoadjuvant chemotherapy, CAD scheme can better predict the pathologic complete response (pCR) of the patients. Second, to more accurately predict prognosis of stroke patients, quantifying brain hemorrhage and ventricular cerebrospinal fluid depicting on brain CT images can play an important role. For this purpose, I developed a new interactive CAD tool to segment hemorrhage regions and extract radiological imaging marker to quantitatively determine the severity of aneurysmal subarachnoid hemorrhage at presentation and correlate the estimation with various homeostatic/metabolic derangements and predict clinical outcome. Third, to improve the efficiency of primary antibody screening processes in new cancer drug development, I developed a CAD scheme to automatically identify the non-negative tissue slides, which indicate reactive antibodies in digital pathology images. Last, to improve operation efficiency and reliability of storing digital pathology image data, I developed a CAD scheme using optical character recognition algorithm to automatically extract metadata from tissue slide label images and reduce manual entry for slide tracking and archiving in the tissue pathology laboratories. In summary, in these studies, we developed and tested several innovative approaches to identify quantitative imaging markers with high discriminatory power. In all CAD schemes, the graphic user interface-based visual aid tools were also developed and implemented. Study results demonstrated feasibility of applying CAD technology to several new application fields, which has potential to assist radiologists, oncologists and pathologists improving accuracy and consistency in disease diagnosis and prognosis assessment of using medical image

Surface Reconstruction from Noisy and Sparse Data

We introduce a set of algorithms for registering, filtering and measuring the similarity of unorganized 3d point clouds, usually obtained from multiple views. We contribute a method for computing the similarity between point clouds that represent closed surfaces, specifically segmented tumors from CT scans. We obtain watertight surfaces and utilize volumetric overlap to determine similarity in a volumetric way. This similarity measure is used to quantify treatment variability based on target volume segmentation both prior to and following radiotherapy planning stages. We also contribute an algorithm for the drift-free registration of thin, non- rigid scans, where drift is the build-up of error caused by sequential pairwise registration, which is the alignment of each scan to its neighbor. We construct an average scan using mutual nearest neighbors, each scan is registered to this average scan, after which we update the average scan and continue this process until convergence. The use case herein is for merging scans of plants from multiple views and registering vascular scans together. Our final contribution is a method for filtering noisy point clouds, specif- ically those constructed from merged depth maps as obtained from a range scanner or multiple view stereo (MVS), applying techniques that have been utilized in finding outliers in clustered data, but not in MVS. We utilize ker- nel density estimation to obtain a probability density function over the space of observed points, utilizing variable bandwidths based on the nature of the neighboring points, Mahalanobis and reachability distances that is more dis- criminative than a classical Mahalanobis distance-based metric

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention