Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

KNOWLEDGE FUSION IN ALGORITHMS FOR MEDICAL IMAGE ANALYSIS

Medical imaging is one of the primary modalities used for clinical diagnosis and treatment planning. Building up a reliable automatic system to assist clinicians read the enormous amount of images benefits the efficiency and accuracy in general clinical trail. Recently deep learning techniques have been widely applied on medical images, but for applications in real clinical scenario, the accuracy, robustness, interpretability of those algorithms requires further validation. In this dissertation, we introduce different strategies of knowledge fusion for improving current approaches in various tasks in medical image analysis. (i) To improve the robustness of segmentation algorithm, we propose to learn the shape prior for organ segmentation and apply it for automatic quality assessment. (ii) To detect pancreatic lesion with patient-level label only, we propose to extract shape and texture information from CT scans and combine them with a fusion network. (iii) In image registration, semantic information is important yet hard to obtain. We propose two methods for introducing semantic knowledge without the need of segmentation label. The first one designs a joint framework for registration synthesis and segmentation to share knowledge between different tasks. The second one introduces unsupervised semantic embedding to improve regular registration framework. (iv) To reduce the false positives in tumor detection task, we propose a hybrid feature engineering system extracting features of the tumor candidates from various perspectives and merging them in the decision stage

U-Net based deep convolutional neural network models for liver segmentation from CT scan images

Liver segmentation is a critical task for diagnosis, treatment and follow-up processes of liver cancer. Computed Tomography (CT) scans are the common medical image modality for the segmentation task. Liver segmentation is considered a very hard task for many reasons. Medical images are limited for researchers. Liver shape is changing based on the patient position during the CT scan process, and varies from patient to another based on the health conditions. Liver and other organs, for example heart, stomach, and pancreas, share similar gray scale range in CT images. Liver treatment using surgery operations is very critical because liver contains significant amount of blood and the position of liver is very close to critical organs like heart, lungs, stomach, and crucial blood veins. Therefore the accuracy of segmentation is critical to define liver and tumors shape and position especially when the treatment surgery conducted using radio frequency heating or cryoablation needles. In the literature, convolutional neural networks (CNN) have achieved very high accuracy on liver segmentation and the U-Net model is considered the state-of-the-art for the medical image segmentation task. Many researchers have developed CNN models based on U-Net and stacked U-Nets with/without bridged connections. However, CNN models need significant number of labeled samples for training and validation which is not commonly available in the case of liver CT images. The process of generating manual annotated masks for the training samples are time consuming and need involvement of expert clinical doctors. Data augmentation has thus been widely used in boosting the sample size for model training. Using rotation with steps of 15o and horizontal and vertical flipping as augmentation techniques, the lack of dataset and training samples issue is solved. The choice of rotation and flipping because in the real life situations, most of the CT scans recorded while the while patient lies on face down or with 45o, 60o,90o on right side according to the location of the tumor. Nonetheless, such process has brought up a new issue for liver segmentation. For example, due to the augmentation operations of rotation and flipping, the trained model detected part of the heart as a liver when it is on the wrong side of the body. The first part of this research conducted an extensive experimental study of U-Net based model in terms of deeper and wider, and variant bridging and skip-connections in order to give recommendation for using U-Net based models. Top-down and bottom-up approaches were used to construct variations of deeper models, whilst two, three, and four stacked U-Nets were applied to construct the wider U-Net models. The variation of the skip connections between two and three U-Nets are the key factors in the study. The proposed model used 2 bridged U-Nets with three extra skip connections between the U-Nets to overcome the flipping issue. A new loss function based on minimizing the distance between the center of mass between the predicted blobs has also enhanced the liver segmentation accuracy. Finally, the deep-supervision concept was integrated with the new loss functions where the total loss was calculated as the sum of weighted loss functions over each weighted deeply supervision. It has achieved a segmentation accuracy of up to 90%. The proposed model of 2 bridged U-Nets with compound skip-connections and specific number of levels, layers, filters, and image size has increased the accuracy of liver segmentation to ~90% whereas the original U-Net and bridged nets have recorded a segmentation accuracy of ~85%. Although applying extra deeply supervised layers and weighted compound of dice coefficient and centroid loss functions solved the flipping issue with ~93%, there is still a room for improving the accuracy by applying some image enhancement as pre-processing stage

U-Net based deep convolutional neural network models for liver segmentation from CT scan images

Liver segmentation is a critical task for diagnosis, treatment and follow-up processes of liver cancer. Computed Tomography (CT) scans are the common medical image modality for the segmentation task. Liver segmentation is considered a very hard task for many reasons. Medical images are limited for researchers. Liver shape is changing based on the patient position during the CT scan process, and varies from patient to another based on the health conditions. Liver and other organs, for example heart, stomach, and pancreas, share similar gray scale range in CT images. Liver treatment using surgery operations is very critical because liver contains significant amount of blood and the position of liver is very close to critical organs like heart, lungs, stomach, and crucial blood veins. Therefore the accuracy of segmentation is critical to define liver and tumors shape and position especially when the treatment surgery conducted using radio frequency heating or cryoablation needles. In the literature, convolutional neural networks (CNN) have achieved very high accuracy on liver segmentation and the U-Net model is considered the state-of-the-art for the medical image segmentation task. Many researchers have developed CNN models based on U-Net and stacked U-Nets with/without bridged connections. However, CNN models need significant number of labeled samples for training and validation which is not commonly available in the case of liver CT images. The process of generating manual annotated masks for the training samples are time consuming and need involvement of expert clinical doctors. Data augmentation has thus been widely used in boosting the sample size for model training. Using rotation with steps of 15o and horizontal and vertical flipping as augmentation techniques, the lack of dataset and training samples issue is solved. The choice of rotation and flipping because in the real life situations, most of the CT scans recorded while the while patient lies on face down or with 45o, 60o,90o on right side according to the location of the tumor. Nonetheless, such process has brought up a new issue for liver segmentation. For example, due to the augmentation operations of rotation and flipping, the trained model detected part of the heart as a liver when it is on the wrong side of the body. The first part of this research conducted an extensive experimental study of U-Net based model in terms of deeper and wider, and variant bridging and skip-connections in order to give recommendation for using U-Net based models. Top-down and bottom-up approaches were used to construct variations of deeper models, whilst two, three, and four stacked U-Nets were applied to construct the wider U-Net models. The variation of the skip connections between two and three U-Nets are the key factors in the study. The proposed model used 2 bridged U-Nets with three extra skip connections between the U-Nets to overcome the flipping issue. A new loss function based on minimizing the distance between the center of mass between the predicted blobs has also enhanced the liver segmentation accuracy. Finally, the deep-supervision concept was integrated with the new loss functions where the total loss was calculated as the sum of weighted loss functions over each weighted deeply supervision. It has achieved a segmentation accuracy of up to 90%. The proposed model of 2 bridged U-Nets with compound skip-connections and specific number of levels, layers, filters, and image size has increased the accuracy of liver segmentation to ~90% whereas the original U-Net and bridged nets have recorded a segmentation accuracy of ~85%. Although applying extra deeply supervised layers and weighted compound of dice coefficient and centroid loss functions solved the flipping issue with ~93%, there is still a room for improving the accuracy by applying some image enhancement as pre-processing stage

Computational Histopathology Analysis based on Deep Learning

Pathology has benefited from the rapid progress in technology of digital scanning during the last decade. Nowadays, slide scanners are able to produce super-resolution whole slide images (WSI), also called digital slides, which can be explored by image viewers as an alternative to the use of conventional microscope. The use of WSI together with the other microscopic and molecular pathology images brings the development of digital pathology, which further enables to perform digital diagnostics. Moreover, the availability of WSI makes it possible to apply image processing and recognition techniques to support digital diagnostics, opening new revenues of computational pathology. However, there still remain many challenging tasks towards computational pathology such as automated cancer categorisation, tumour area segmentation, and cell-level instance detection. In this study, we explore problems related to the above tasks in histology images. Cancer categorisation can be addressed as a histopathological image classification problem. Multiple aspects such as variations caused by magnification factors and class imbalance make it a challenging task where conventional methods cannot obtain satisfactory performance in many cases. We propose to learn similarity-based embeddings for magnification-independent cancer categorisation. A pair loss and a triplet loss are proposed to learn embeddings that can measure similarity between images for classification. Furthermore, to eliminate the impact of class imbalance, instead of using the strategy of hard samples mining that intuitively discard some easy samples, we introduce a new loss function to simultaneously punish hard misclassified samples and suppress easy well-classified samples. Tumour area segmentation in whole-slide images is a fundamental step for viable tumour burden estimation, which is of great value for cancer assessment. Vague boundaries and small regions dissociated from viable tumour areas are two main challenges to accurately segment tumour area. We present a structure-aware scale-adaptive feature selection method for efficient and accurate tumour area segmentation. Specifically, based on a segmentation network with a popular encoder-decoder architecture, a scale-adaptive module is proposed to select more robust features to represent the vague, non-rigid boundaries. Furthermore, a structural similarity metric is proposed for better tissue structure awareness to deal with small region segmentation. Detection of cell-level instances in histology images is essential to acquire morphological and numeric clues for cancer assessment. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional object detection methods cannot obtain satisfactory performance in many cases. We propose similarity-based region proposal networks for nuclei and cells detection in histology images. In particular, a customized convolution layer termed as embedding layer is designed for network building. The embedding layer is then added on to modify the region proposal networks, which enables the networks to learn discriminative features based on similarity learning

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