Analysis of sparsity- and nonlocality-reinforced convolutional neural networks for image denoising

Different neural networks are built using the same elements, but they vary a lot by architecture and show different denoising quality results. The master thesis aims to analyze how different network hyperparameters, input transformations (sparsity), and nonlocal filters (nonlocality) impact the performance in image denoising tasks. The thesis work provides rich experimentally based research and analysis in the image denoising field. A number of different denoising methods have been considered, and state-of-the-art denoising algorithms were examined. As a result of the parameter analysis, the vast majority of the learning-based algorithms and deep networks were improved in terms of denoising capability

Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones

Nowadays, due to the widespread use of smartphones in everyday life and the improvement of computational capabilities of these devices, many complex tasks can now be deployed on them. Concerning the need for continuous monitoring of vital signs, especially for the elderly or those with certain types of diseases, the development of algorithms that can estimate vital signs using smartphones has attracted researchers worldwide. Such algorithms estimate vital signs (heart rate and oxygen saturation level) by processing an input PPG signal. These methods often apply multiple pre-processing steps to the input signal before the prediction step. This can increase the computational complexity of these methods, meaning only a limited number of mobile devices can run them. Furthermore, multiple pre-processing steps also require the design of a couple of hand-crafted stages to obtain an optimal result. This research proposes a novel end-to-end solution to mobile-based vital sign estimation by deep learning. The proposed method does not require any pre-processing. Due to the use of fully convolutional architecture, the parameter count of our proposed model is, on average, a quarter of the ordinary architectures that use fully-connected layers as the prediction heads. As a result, the proposed model has less over-fitting chance and computational complexity. A public dataset for vital sign estimation, including 62 videos collected from 35 men and 27 women, is also provided. The experimental results demonstrate state-of-the-art estimation accuracy.Comment: 6 pages, 9 figures, 4 table

Automatic Segmentation of Intramedullary Multiple Sclerosis Lesions

Contexte: La moelle épinière est un composant essentiel du système nerveux central. Elle contient des neurones responsables d’importantes fonctionnalités et assure la transmission d’informations motrices et sensorielles entre le cerveau et le système nerveux périphérique. Un endommagement de la moelle épinière, causé par un choc ou une maladie neurodégénérative, peut mener à un sérieux handicap, pouvant entraîner des incapacités fonctionnelles, de la paralysie et/ou de la douleur. Chez les patients atteints de sclérose en plaques (SEP), la moelle épinière est fréquemment affectée par de l’atrophie et/ou des lésions. L’imagerie par résonance magnétique (IRM) conventionnelle est largement utilisée par des chercheurs et des cliniciens pour évaluer et caractériser, de façon non-invasive, des altérations micro-structurelles. Une évaluation quantitative des atteintes structurelles portées à la moelle épinière (e.g. sévérité de l’atrophie, extension des lésions) est essentielle pour le diagnostic, le pronostic et la supervision sur le long terme de maladies, telles que la SEP. De plus, le développement de biomarqueurs impartiaux est indispensable pour évaluer l’effet de nouveaux traitements thérapeutiques. La segmentation de la moelle épinière et des lésions intramédullaires de SEP sont, par conséquent, pertinentes d’un point de vue clinique, aussi bien qu’une étape nécessaire vers l’interprétation d’images RM multiparamétriques. Cependant, la segmentation manuelle est une tâche extrêmement chronophage, fastidieuse et sujette à des variations inter- et intra-expert. Il y a par conséquent un besoin d’automatiser les méthodes de segmentations, ce qui pourrait faciliter l’efficacité procédures d’analyses. La segmentation automatique de lésions est compliqué pour plusieurs raisons: (i) la variabilité des lésions en termes de forme, taille et position, (ii) les contours des lésions sont la plupart du temps difficilement discernables, (iii) l’intensité des lésions sur des images MR sont similaires à celles de structures visiblement saines. En plus de cela, réaliser une segmentation rigoureuse sur l’ensemble d’une base de données multi-centrique d’IRM est rendue difficile par l’importante variabilité des protocoles d’acquisition (e.g. résolution, orientation, champ de vue de l’image). Malgré de considérables récents développements dans le traitement d’images MR de moelle épinière, il n’y a toujours pas de méthode disponible pouvant fournir une segmentation rigoureuse et fiable de la moelle épinière pour un large spectre de pathologies et de protocoles d’acquisition. Concernant les lésions intramédullaires, une recherche approfondie dans la littérature n’a pas pu fournir une méthode disponible de segmentation automatique. Objectif: Développer un système complètement automatique pour segmenter la moelle épinière et les lésions intramédullaires sur des IRM conventionnelles humaines. Méthode: L’approche présentée est basée de deux réseaux de neurones à convolution mis en cascade. La méthode a été pensée pour faire face aux principaux obstacles que présentent les données IRM de moelle épinière. Le procédé de segmentation a été entrainé et validé sur une base de données privée composée de 1943 images, acquises dans 30 différents centres avec des protocoles hétérogènes. Les sujets scannés comportent 459 sujets sains, 471 patients SEP et 112 avec d’autres pathologies affectant la moelle épinière. Le module de segmentation de la moelle épinière a été comparé à une méthode existante reconnue par la communauté, PropSeg. Résultats: L’approche basée sur les réseaux de neurones à convolution a fourni de meilleurs résultats que PropSeg, atteignant un Dice médian (intervalle inter-quartiles) de 94.6 (4.6) vs. 87.9 (18.3) %. Pour les lésions, notre segmentation automatique a permis d'obtenir un Dice de 60.0 (21.4) % en le comparant à la segmentation manuelle, un ratio de vrai positifs de 83 (34) %, et une précision de 77 (44) %. Conclusion: Une méthode complètement automatique et innovante pour segmenter la moelle épinière et les lésions SEP intramédullaires sur des données IRM a été conçue durant ce projet de maîtrise. La méthode a été abondamment validée sur une base de données clinique. La robustesse de la méthode de segmentation de moelle épinière a été démontrée, même sur des cas pathologiques. Concernant la segmentation des lésions, les résultats sont encourageants, malgré un taux de faux positifs relativement élevé. Je crois en l’impact que peut potentiellement avoir ces outils pour la communauté de chercheurs. Dans cette optique, les méthodes ont été intégrées et documentées dans un logiciel en accès-ouvert, la “Spinal Cord Toolbox”. Certains des outils développés pendant ce projet de Maîtrise sont déjà utilisés par des analyses d’études cliniques, portant sur des patients SEP et sclérose latérale amyotrophique.----------ABSTRACT Context: The spinal cord is a key component of the central nervous system, which contains neurons responsible for complex functions, and ensures the conduction of motor and sensory information between the brain and the peripheral nervous system. Damage to the spinal cord, through trauma or neurodegenerative diseases, can lead to severe impairment, including functional disabilities, paralysis and/or pain. In multiple sclerosis (MS) patients, the spinal cord is frequently affected by atrophy and/or lesions. Conventional magnetic resonance imaging (MRI) is widely used by researchers and clinicians to non-invasively assess and characterize spinal cord microstructural changes. Quantitative assessment of the structural damage to the spinal cord (e.g. atrophy severity, lesion extent) is essential for the diagnosis, prognosis and longitudinal monitoring of diseases, such as MS. Furthermore, the development of objective biomarkers is essential to evaluate the effect of new therapeutic treatments. Spinal cord and intramedullary MS lesions segmentation is consequently clinically relevant, as well as a necessary step towards the interpretation of multi-parametric MR images. However, manual segmentation is highly time-consuming, tedious and prone to intra- and inter-rater variability. There is therefore a need for automated segmentation methods to facilitate the efficiency of analysis pipelines. Automatic lesion segmentation is challenging for various reasons: (i) lesion variability in terms of shape, size and location, (ii) lesion boundaries are most of the time not well defined, (iii) lesion intensities on MR data are confounding with those of normal-appearing structures. Moreover, achieving robust segmentation across multi-center MRI data is challenging because of the broad variability of data features (e.g. resolution, orientation, field of view). Despite recent substantial developments in spinal cord MRI processing, there is still no method available that can yield robust and reliable spinal cord segmentation across the very diverse spinal pathologies and data features. Regarding the intramedullary lesions, a thorough search of the relevant literature did not yield available method of automatic segmentation. Goal: To develop a fully-automatic framework for segmenting the spinal cord and intramedullary MS lesions from conventional human MRI data. Method: The presented approach is based on a cascade of two Convolutional Neural Networks (CNN). The method has been designed to face the main challenges of ‘real world’ spinal cord MRI data. It was trained and validated on a private dataset made up of 1943 MR volumes, acquired in different 30 sites with heterogeneous acquisition protocols. Scanned subjects involve 459 healthy controls, 471 MS patients and 112 with other spinal pathologies. The proposed spinal cord segmentation method was compared to a state-of-the-art spinal cord segmentation method, PropSeg. Results: The CNN-based approach achieved better results than PropSeg, yielding a median (interquartile range) Dice of 94.6 (4.6) vs. 87.9 (18.3) % when compared to the manual segmentation. For the lesion segmentation task, our method provided a median Dice-overlap with the manual segmentation of 60.0 (21.4) %, a lesion-based true positive rate of 83 (34) % and a lesion-based precision de 77 (44) %. Conclusion: An original fully-automatic method to segment the spinal cord and intramedullary MS lesions on MRI data has been devised during this Master’s project. The method was validated extensively against a clinical dataset. The robustness of the spinal cord segmentation has been demonstrated, even on challenging pathological cases. Regarding the lesion segmentation, the results are encouraging despite the fairly high false positive rate. I believe in the potential value of these developed tools for the research community. In this vein, the methods are integrated and documented into an open-source software, the Spinal Cord Toolbox. Some of the tools developed during this Master’s project are already integrated into automated analysis pipelines of clinical studies, including MS and Amyotrophic Lateral Sclerosis patients

Region of Interest Localization Methods for Publicly Available Palmprint Databases

So far, there exist many publicly available palmprint databases. However, not all of them have provided the corresponding region of interest (ROI) images. If everyone uses their own extracted ROI images for performance testing, the final accuracy is not strictly comparable. Since ROI localization is the critical stage of palmprint recognition. The location precision has a significant impact on the final recognition accuracy, especially in unconstrained scenarios. This problem has limited the applications of palmprint recognition. However, many currently published surveys only focus on feature extraction and classification methods. Throughout these years, many new ROI localization methods have been proposed. In this chapter, we will group the existing ROI localization methods into different categories, analyze their basic ideas, reproduce some of the codes, make comparisons of their performances, and provide further directions. We hope this could be a useful reference for further research

Deep Learning in Visual Computing and Signal Processing

Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. Besides, this study also discusses the challenges of designing and training deep neural networks