Deep learning assisted MRI guided attenuation correction in PET

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological and functional details of the tissue at the molecular level. However, the acquired PET images have some limitations such as the attenuation. PET attenuation correction is an essential step to obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners, magnetic resonance (MR) images are used to address the problem of PET attenuation correction. The MR images segmentation is one simple and robust approach to create pseudo computed tomography (CT) images, which are used to generate attenuation coefficient maps to correct the PET attenuation. Recently, deep learning has been proposed and used as a promising technique to efficiently perform MR and various medical images segmentation. In this research work, deep learning guided segmentation approaches have been proposed to enhance the bone class segmentation of MR brain images in order to generate accurate pseudo-CT images. The first approach has introduced the combination of handcrafted features with deep learning features to enrich the set of features. Multiresolution analysis techniques, which generate multiscale and multidirectional coefficients of an image such as contourlet and shearlet transforms, are applied and combined with deep convolutional neural network (CNN) features. Different experiments have been conducted to investigate the number of selected coefficients and the insertion location of the handcrafted features. The second approach aims at reducing the segmentation algorithm’s complexity while maintaining the segmentation performance. An attention based convolutional encode-decoder network has been proposed to adaptively recalibrate the deep network features. This attention based network consists of two different squeeze and excitation blocks that excite the features spatially and channel wise. The two blocks are combined sequentially to decrease the number of network’s parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w images. Multiple fine tuning approaches and experiments have been conducted to study the best fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been used to carry an objective evaluation to measure the segmentation performance of the results obtained by the three proposed methods. The first and second approaches have been validated with other studies in the literature that applied deep network based segmentation technique to perform MR based attenuation correction for PET images. The proposed methods have shown an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC) from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%. The proposed excitation-based CNN has decreased the model complexity by decreasing the number of trainable parameters by more than 46% where less computing resources are required to train the model. The proposed hybrid transfer learning method has shown its superiority to build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied transfer learning methods especially with the bone class where the DSC is increased from 0.3841 to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than transfer learning using open and conservative fine tuning

Multiscale and Directional Representations of High-Dimensional Information Content in Remotely Sensed Data

This thesis explores the theory and applications of directional representations in the field of anisotropic harmonic analysis. Although wavelets are optimal for decomposing functions in one dimension, they are unable to achieve the same success in two or more dimensions due to the presence of curves and surfaces of discontinuity. In order to optimally capture the behavior of a function at high-dimensional discontinuities, we must be able to incorporate directional information into our analyzing functions, in addition to location and scale. Examples of such representations are contourlets, curvelets, ridgelets, bandelets, wedgelets, and shearlets. Using directional representations, in particular shearlets, we tackle several challenging problems in the processing of remotely sensed data. First, we detect roads and ditches in LIDAR data of rural scenes. Second, we develop an algorithm for superresolution of optical and hyperspectral data. We conclude by presenting a stochastic particle model in which the probability of movement in a particular direction is neighbor-weighted

Spatio-Temporal Video Analysis and the 3D Shearlet Transform

Abstract The automatic analysis of the content of a video sequence has captured the attention of the computer vision community for a very long time. Indeed, video understanding, which needs to incorporate both semantic and dynamic cues, may be trivial for humans, but it turned out to be a very complex task for a machine. Over the years the signal processing, computer vision, and machine learning communities contributed with algorithms that are today effective building blocks of more and more complex systems. In the meanwhile, theoretical analysis has gained a better understanding of this multifaceted type of data. Indeed, video sequences are not only high dimensional data, but they are also very peculiar, as they include spatial as well as temporal information which should be treated differently, but are both important to the overall process. The work of this thesis builds a new bridge between signal processing theory, and computer vision applications. It considers a novel approach to multi resolution signal processing, the so-called Shearlet Transform, as a reference framework for representing meaningful space-time local information in a video signal. The Shearlet Transform has been shown effective in analyzing multi-dimensional signals, ranging from images to x-ray tomographic data. As a tool for signal denoising, has also been applied to video data. However, to the best of our knowledge, the Shearlet Transform has never been employed to design video analysis algorithms. In this thesis, our broad objective is to explore the capabilities of the Shearlet Transform to extract information from 2D+T-dimensional data. We exploit the properties of the Shearlet decomposition to redesign a variety of classical video processing techniques (including space-time interest point detection and normal flow estimation) and to develop novel methods to better understand the local behavior of video sequences. We provide experimental evidence on the potential of our approach on synthetic as well as real data drawn from publicly available benchmark datasets. The results we obtain show the potential of our approach and encourages further investigations in the near future

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity