Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis

Feature learning with high dimensional neuroimaging features has been explored for the applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental status test scores and cerebrospinal fluid level, are essential in clinical diagnosis of neurological disorders, because they could be simple and effective for the clinicians to assess the disorder’s progression and severity. Rather than only using the low-dimensional biomarkers as inputs for decision making systems, we believe that such low-dimensional biomarkers can be used for enhancing the feature learning pipeline. In this study, we proposed a novel feature representation learning framework, Multi-Phase Feature Representation (MPFR), with low-dimensional biomarkers embedded. MPFR learns high-level neuroimaging features by extracting the associations between the low-dimensional biomarkers and the high-dimensional neuroimaging features with a deep neural network. We validated the proposed framework using the Mini-Mental-State-Examination (MMSE) scores as a low-dimensional biomarker and multi-modal neuroimaging data as the high-dimensional neuroimaging features from the ADNI baseline cohort. The proposed approach outperformed the original neural network in both binary and ternary Alzheimer’s disease classification tasks

Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed

Local Geometric Transformations in Image Analysis

The characterization of images by geometric features facilitates the precise analysis of the structures found in biological micrographs such as cells, proteins, or tissues. In this thesis, we study image representations that are adapted to local geometric transformations such as rotation, translation, and scaling, with a special emphasis on wavelet representations. In the first part of the thesis, our main interest is in the analysis of directional patterns and the estimation of their location and orientation. We explore steerable representations that correspond to the notion of rotation. Contrarily to classical pattern matching techniques, they have no need for an a priori discretization of the angle and for matching the filter to the image at each discretized direction. Instead, it is sufficient to apply the filtering only once. Then, the rotated filter for any arbitrary angle can be determined by a systematic and linear transformation of the initial filter. We derive the Cramér-Rao bounds for steerable filters. They allow us to select the best harmonics for the design of steerable detectors and to identify their optimal radial profile. We propose several ways to construct optimal representations and to build powerful and effective detector schemes; in particular, junctions of coinciding branches with local orientations. The basic idea of local transformability and the general principles that we utilize to design steerable wavelets can be applied to other geometric transformations. Accordingly, in the second part, we extend our framework to other transformation groups, with a particular interest in scaling. To construct representations in tune with a notion of local scale, we identify the possible solutions for scalable functions and give specific criteria for their applicability to wavelet schemes. Finally, we propose discrete wavelet frames that approximate a continuous wavelet transform. Based on these results, we present a novel wavelet-based image-analysis software that provides a fast and automatic detection of circular patterns, combined with a precise estimation of their size

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

SEGMENTATION AND INFORMATICS IN MULTIDIMENSIONAL FLUORESCENCE OPTICAL MICROSCOPY IMAGES

Recent advances in the field of optical microscopy have enabled scientists to observe and image complex biological processes across a wide range of spatial and temporal resolution, resulting in an exponential increase in optical microscopy data. Manual analysis of such large volumes of data is extremely time consuming and often impossible if the changes cannot be detected by the human eye. Naturally it is essential to design robust, accurate and high performance image processing and analysis tools to extract biologically significant results. Furthermore, the presentation of the results to the end-user, post analysis, is also an equally challenging issue, especially when the data (and/or the hypothesis) involves several spatial/hierarchical scales (e.g., tissues, cells, (sub)-nuclear components). This dissertation concentrates on a subset of such problems such as robust edge detection, automatic nuclear segmentation and selection in multi-dimensional tissue images, spatial analysis of gene localization within the cell nucleus, information visualization and the development of a computational framework for efficient and high-throughput processing of large datasets. Initially, we have developed 2D nuclear segmentation and selection algorithms which help in the development of an integrated approach for determining the preferential spatial localization of certain genes within the cell nuclei which is emerging as a promising technique for the diagnosis of breast cancer. Quantification requires accurate segmentation of 100 to 200 cell nuclei in each patient tissue sample in order to draw a statistically significant result. Thus, for large scale analysis involving hundreds of patients, manual processing is too time consuming and subjective. We have developed an integrated workflow that selects, following 2D automatic segmentation, a sub-population of accurately delineated nuclei for positioning of fluorescence in situ hybridization labeled genes of interest in tissue samples. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all 4 normal cases and all 5 non-cancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach. As a natural progression from the 2D analysis algorithms to 3D, we first developed a robust and accurate probabilistic edge detection method for 3D tissue samples since several down stream analysis procedures such as segmentation and tracking rely on the performance of edge detection. The method based on multiscale and multi-orientation steps surpasses several other conventional edge detectors in terms of its performance. Subsequently, given an appropriate edge measure, we developed an optimal graphcut-based 3D nuclear segmentation technique for samples where the cell nuclei are volume or surface labeled. It poses the problem as one of finding minimal closure in a directed graph and solves it efficiently using the maxflow-mincut algorithm. Both interactive and automatic versions of the algorithm are developed. The algorithm outperforms, in terms of three metrics that are commonly used to evaluate segmentation algorithms, a recently reported geodesic distance transform-based 3D nuclear segmentation method which in turns was reported to outperform several other popular tools that segment 3D nuclei in tissue samples. Finally, to apply some of the aforementioned methods to large microscopic datasets, we have developed a user friendly computing environment called MiPipeline which supports high throughput data analysis, data and process provenance, visual programming and seamlessly integrated information visualization of hierarchical biological data. The computational part of the environment is based on LONI Pipeline distributed computing server and the interactive information visualization makes use of several javascript based libraries to visualize an XML-based backbone file populated with essential meta-data and results

Deep learning-based diagnostic system for malignant liver detection

Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent, accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification. In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms. However, such traditional methods could immensely affect the structural properties of processed images with inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use. To address these limitations, I propose novel methodologies in this dissertation. First, I modified a generative adversarial network to perform deblurring and contrast adjustment on computed tomography (CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver detection. The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods. The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification. A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions. Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants. In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore, the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis