A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Automatic methods for detection of midline brain abnormalities

En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV)Different studies have demonstrated an augmented prevalence of different midline brain abnormalities in patients with both mood and psychotic disorders. One of these abnormalities is the cavum septum pellucidum (CSP), which occurs when the septum pellucidum fails to fuse. The study of these abnormalities in Magnetic Resonance Imaging requires a tedious and time-consuming process of manually analyzing all the images in order to detect them. It is also problematic when the same abnormality is analyzed manually by different experts because different criteria can be applied. In this context, in would be useful to develop an automatic method for localizing the abnormality and give the measure of its depth. In this project, several methods have been developed by using machine learning and deep learning techniques. To test the proposed approaches, data from of 861 subjects from FIDMAG, have been gathered and processed to use with our algorithms. Among the subjects, 639 are patients with mood or psychotic disorders and 223 are healthy controls. This same dataset was previously used in a study where the authors analyzed the prevalence of this abnormality across the subjects manually, the depth of the abnormality previously reported is finally compared with the results obtained from our automatic methods. The first proposed method implemented is an unsupervised algorithm that segments the abnormality over 2D slices with an accuracy of the 89.72% and a sensitivity of an 84%. Then, a comparison among different Machine Learning classifiers have been conducted along with different dimensionality reduction methods. Between them, K Nearest Neighbors produced the best overall accuracy, with a 99.1% of accuracy and a sensitivity of a 99% by using HOG descriptors to reduce the dimensionality of the data. The last group of methods are different Deep Learning architectures. Three different implementations have been designed, and over the best performing architecture, two different input options to take into account contextual information have been proposed. The best Deep Learning method reached an accuracy of 98,8% and a specificity and sensitivity of a 99%. Finally, an analysis of the similarity with respect to the results with the manually handcrafted ground-truth have been conducted to validate the usability of the automatic method in further analysis of the abnormality

A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed

Segmentation of images with low-contrast edges

A vast amount of the current research in medical image analysis has aimed to develop improved techniques of image segmentation. Of the existing approaches, active contour methods have proven effective by incorporating edge or region information from the image into a level set formulation. However, complications arise in images containing regions of low-contrast due to noise, occlusions, or partial volume effects, which are often unavoidable in practical applications. Incorporating prior shape information into the segmentation framework provides a more accurate and robust solution by constraining the evolving contour to resemble a target shape. Two methods are presented to incorporate a shape prior into existing active contour segmentation methods, including the edge-based geodesic active contours model and a fast update implementation of the region-based Chan-Vese model. Applying these methods to synthetic and real images demonstrates that an improved result can be obtained for images containing low-contrast edge regions

Surface loss for medical image segmentation

Last decades have witnessed an unprecedented expansion of medical data in various largescale and complex systems. While achieving a lot of successes in many complex medical problems, there are still some challenges to deal with. Class imbalance is one of the common problems of medical image segmentation. It occurs mostly when there is a severely unequal class distribution, for instance, when the size of target foreground region is several orders of magnitude less that the background region size. In such problems, typical loss functions used for convolutional neural networks (CNN) segmentation fail to deliver good performances. Widely used losses,e.g., Dice or cross-entropy, are based on regional terms. They assume that all classes are equally distributed. Thus, they tend to favor the majority class and misclassify the target class. To address this issue, the main objective of this work is to build a boundary loss, a distance based measure on the space of contours and not regions. We argue that a boundary loss can mitigate the problems of regional losses via introducing a complementary distance-based information. Our loss is inspired by discrete (graph-based) optimization techniques for computing gradient flows of curve evolution. Following an integral approach for computing boundary variations, we express a non-symmetric L2 distance on the space of shapes as a regional integral, which avoids completely local differential computations. Our boundary loss is the sum of linear functions of the regional softmax probability outputs of the network. Therefore, it can easily be combined with standard regional losses and implemented with any existing deep network architecture for N-dimensional segmentation (N-D). Experiments were carried on three benchmark datasets corresponding to increasingly unbalanced segmentation problems: Multi modal brain tumor segmentation (BRATS17), the ischemic stroke lesion (ISLES) and white matter hyperintensities (WMH). Used in conjunction with the region-based generalized Dice loss (GDL), our boundary loss improves performance significantly compared to GDL alone, reaching up to 8% improvement in Dice score and 10% improvement in Hausdorff score. It also yielded a more stable learning process

Computer aided assessment of CT scans of traumatic brain injury patients

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyOne of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues