Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics

Current research in the life sciences involves the analysis of such a huge amount of image data that automatization is required. This thesis presents several ways how pattern recognition techniques may contribute to improved tumor diagnostics and to the elucidation of vertebrate embryonic development. Chapter 1 studies an approach for exploiting spatial context for the improved estimation of metabolite concentrations from magnetic resonance spectroscopy imaging (MRSI) data with the aim of more robust tumor detection, and compares against a novel alternative. Chapter 2 describes a software library for training, testing and validating classification algorithms that estimate tumor probability based on MRSI. It allows flexible adaptation towards changed experimental conditions, classifier comparison and quality control without need for expertise in pattern recognition. Chapter 3 studies several models for learning tumor classifiers that allow for the common unreliability of human segmentations. For the first time, models are used for this task that additionally employ the objective image information. Chapter 4 encompasses two contributions to an image analysis pipeline for automatically reconstructing zebrafish embryonic development based on time-resolved microscopy: Two approaches for nucleus segmentation are experimentally compared, and a procedure for tracking nuclei over time is presented and evaluated

MRI-Based Attenuation Correction in Emission Computed Tomography

The hybridization of magnetic resonance imaging (MRI) with positron emission tomography (PET) or single photon emission computed tomography (SPECT) enables the collection of an assortment of biological data in spatial and temporal register. However, both PET and SPECT are subject to photon attenuation, a process that degrades image quality and precludes quantification. To correct for the effects of attenuation, the spatial distribution of linear attenuation coefficients (μ-coefficients) within and about the patient must be available. Unfortunately, extracting μ-coefficients from MRI is non-trivial. In this thesis, I explore the problem of MRI-based attenuation correction (AC) in emission tomography. In particular, I began by asking whether MRI-based AC would be more reliable in PET or in SPECT. To this end, I implemented an MRI-based AC algorithm relying on image segmentation and applied it to phantom and canine emission data. The subsequent analysis revealed that MRI-based AC performed better in SPECT than PET, which is interesting since AC is more challenging in SPECT than PET. Given this result, I endeavoured to improve MRI-based AC in PET. One problem that required addressing was that the lungs yield very little signal in MRI, making it difficult to infer their μ-coefficients. By using a pulse sequence capable of visualizing lung parenchyma, I established a linear relationship between MRI signal and the lungs’ μ-coefficients. I showed that applying this mapping on a voxel-by-voxel basis improved quantification in PET reconstructions compared to conventional MRI-based AC techniques. Finally, I envisaged that a framework for MRI-based AC methods would potentiate further improvements. Accordingly, I identified three ways an MRI can be converted to μ-coefficients: 1) segmentation, wherein the MRI is divided into tissue types and each is assigned an μ-coefficient, 2) registration, wherein a template of μ-coefficients is aligned with the MRI, and 3) mapping, wherein a function maps MRI voxels to μ-coefficients. I constructed an algorithm for each method and catalogued their strengths and weaknesses. I concluded that a combination of approaches is desirable for MRI-based AC. Specifically, segmentation is appropriate for air, fat, and water, mapping is appropriate for lung, and registration is appropriate for bone

Development of a 3D Mouse Atlas Tool for Improved Non-Invasive Imaging of Orthotopic Mouse Models of Pancreatic Cancer.

PhD ThesesPancreatic cancer is the 10th most common cancer in the UK with 10,000 people a year being diagnosed. This form of cancer also has one of the lowest survival rates, with only 5% of patient surviving for 5 years (1). There has not been significant progress in the treatment of pancreatic cancer for the last 30 years (1). Recognition of this historic lack of progress has led to an increase in research effort and funding aimed at developing novel treatments for pancreatic cancer. This in turn has had an inflationary effect on the numbers of animals being used to study the effects of these treatments. Genetically engineered mouse models (GEMMs) are currently thought to be most appropriate for these types of studies as the manner in which the mice develop pancreatic tumours is much closer to that seen in the clinic. One such GEMM is the K-rasLSL.G12D/+;p53R172H/+;PdxCre (KPC) model (2) in which the mouse is born with normal pancreas and then develops PanIN lesions (one of the main lesions linked to pancreatic ductal adenocarcinoma (PDAC) (2)) at an accelerated rate. The KPC model is immune competent and because the tumours develop orthotopically in the pancreas, they have a relevant microenvironment and stromal makeup, suitable for testing of new therapeutic approaches. Unlike the human pancreas which is regular in shape, the mouse pancreas is a soft and spongy organ that has its dimensions defined to a large extent by the position of the organs that surround it, such as the kidney, stomach and spleen (3). This changes as pancreatic tumours develop, with the elasticity of the pancreas decreasing as the tissue becomes more desmoplastic. Because the tumours are deep within the body, disease burden is difficult to assess except by sacrificing groups of animals or by using non-invasive imaging. Collecting data by sacrificing groups of animals at different timepoints results in use of very high numbers per study. This is in addition to the fact that in the KPC model (similar to other GEMMs), fewer than 25% have the desired genetic makeup, meaning that 3-4 animals are destroyed for every one that is put into study (2). Therefore, in order to reduce the numbers of animals used in 5 pancreatic research, a non-invasive imaging tool that allows accurate assessment of pancreatic tumour burden longitudinally over time has been developed. Magnetic resonance imaging (MRI) has been used as it is not operator dependent (allowing it to be used by non-experts) and does not use ionising radiation which is a potential confounding factor when monitoring tumour development. The tool has been developed for use with a low field instrument (1T) which ensures its universal applicability as it will perform even better when used with magnets of field strength higher than 1T. This work has been carried out starting from an existing 3D computational mouse atlas and developing a mathematical model that can automatically detect and segment mouse pancreas as well as pancreatic tumours in MRI images. This has been achieved using multiple image analysis techniques including thresholding, texture analysis, object detection, edge detection, multi-atlas segmentation, and machine learning. Through these techniques, unnecessary information is removed from the image, the area of analysis is reduced, the pancreas is isolated (and then classified healthy or unhealthy), and - if unhealthy - the pancreas is evaluated to identify tumour location and volume. This semi-automated approach aims to aid researchers by reducing image analysis time (especially for non-expert users) and increasing both objectivity and statistical accuracy. It facilitates the use of MRI as a method of longitudinally tracking tumour development and measuring response to therapy in the same animal, thus reducing biological variability and leading to a reduction in group size. The MR images of mice and pancreatic tumours used in this work were obtained through studies already being conducted in order to reduce the number of animals used without having to compromise on the validity of results

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

Segmentation and Characterization of Small Retinal Vessels in Fundus Images Using the Tensor Voting Approach

RÉSUMÉ La rétine permet de visualiser facilement une partie du réseau vasculaire humain. Elle offre ainsi un aperçu direct sur le développement et le résultat de certaines maladies liées au réseau vasculaire dans son entier. Chaque complication visible sur la rétine peut avoir un impact sur la capacité visuelle du patient. Les plus petits vaisseaux sanguins sont parmi les premières structures anatomiques affectées par la progression d’une maladie, être capable de les analyser est donc crucial. Les changements dans l’état, l’aspect, la morphologie, la fonctionnalité, ou même la croissance des petits vaisseaux indiquent la gravité des maladies. Le diabète est une maladie métabolique qui affecte des millions de personnes autour du monde. Cette maladie affecte le taux de glucose dans le sang et cause des changements pathologiques dans différents organes du corps humain. La rétinopathie diabétique décrit l’en- semble des conditions et conséquences du diabète au niveau de la rétine. Les petits vaisseaux jouent un rôle dans le déclenchement, le développement et les conséquences de la rétinopa- thie. Dans les dernières étapes de cette maladie, la croissance des nouveaux petits vaisseaux, appelée néovascularisation, présente un risque important de provoquer la cécité. Il est donc crucial de détecter tous les changements qui ont lieu dans les petits vaisseaux de la rétine dans le but de caractériser les vaisseaux sains et les vaisseaux anormaux. La caractérisation en elle-même peut faciliter la détection locale d’une rétinopathie spécifique. La segmentation automatique des structures anatomiques comme le réseau vasculaire est une étape cruciale. Ces informations peuvent être fournies à un médecin pour qu’elles soient considérées lors de son diagnostic. Dans les systèmes automatiques d’aide au diagnostic, le rôle des petits vaisseaux est significatif. Ne pas réussir à les détecter automatiquement peut conduire à une sur-segmentation du taux de faux positifs des lésions rouges dans les étapes ultérieures. Les efforts de recherche se sont concentrés jusqu’à présent sur la localisation précise des vaisseaux de taille moyenne. Les modèles existants ont beaucoup plus de difficultés à extraire les petits vaisseaux sanguins. Les modèles existants ne sont pas robustes à la grande variance d’apparence des vaisseaux ainsi qu’à l’interférence avec l’arrière-plan. Les modèles de la littérature existante supposent une forme générale qui n’est pas suffisante pour s’adapter à la largeur étroite et la courbure qui caractérisent les petits vaisseaux sanguins. De plus, le contraste avec l’arrière-plan dans les régions des petits vaisseaux est très faible. Les méthodes de segmentation ou de suivi produisent des résultats fragmentés ou discontinus. Par ailleurs, la segmentation des petits vaisseaux est généralement faite aux dépends de l’amplification du bruit. Les modèles déformables sont inadéquats pour segmenter les petits vaisseaux. Les forces utilisées ne sont pas assez flexibles pour compenser le faible contraste, la largeur, et vii la variance des vaisseaux. Enfin, les approches de type apprentissage machine nécessitent un entraînement avec une base de données étiquetée. Il est très difficile d’obtenir ces bases de données dans le cas des petits vaisseaux. Cette thèse étend les travaux de recherche antérieurs en fournissant une nouvelle mé- thode de segmentation des petits vaisseaux rétiniens. La détection de ligne à échelles multiples (MSLD) est une méthode récente qui démontre une bonne performance de segmentation dans les images de la rétine, tandis que le vote tensoriel est une méthode proposée pour reconnecter les pixels. Une approche combinant un algorithme de détection de ligne et de vote tensoriel est proposée. L’application des détecteurs de lignes a prouvé son efficacité à segmenter les vais- seaux de tailles moyennes. De plus, les approches d’organisation perceptuelle comme le vote tensoriel ont démontré une meilleure robustesse en combinant les informations voisines d’une manière hiérarchique. La méthode de vote tensoriel est plus proche de la perception humain que d’autres modèles standards. Comme démontré dans ce manuscrit, c’est un outil pour segmenter les petits vaisseaux plus puissant que les méthodes existantes. Cette combinaison spécifique nous permet de surmonter les défis de fragmentation éprouvés par les méthodes de type modèle déformable au niveau des petits vaisseaux. Nous proposons également d’utiliser un seuil adaptatif sur la réponse de l’algorithme de détection de ligne pour être plus robuste aux images non-uniformes. Nous illustrons également comment une combinaison des deux méthodes individuelles, à plusieurs échelles, est capable de reconnecter les vaisseaux sur des distances variables. Un algorithme de reconstruction des vaisseaux est également proposé. Cette dernière étape est nécessaire car l’information géométrique complète est requise pour pouvoir utiliser la segmentation dans un système d’aide au diagnostic. La segmentation a été validée sur une base de données d’images de fond d’oeil à haute résolution. Cette base contient des images manifestant une rétinopathie diabétique. La seg- mentation emploie des mesures de désaccord standards et aussi des mesures basées sur la perception. En considérant juste les petits vaisseaux dans les images de la base de données, l’amélioration dans le taux de sensibilité que notre méthode apporte par rapport à la méthode standard de détection multi-niveaux de lignes est de 6.47%. En utilisant les mesures basées sur la perception, l’amélioration est de 7.8%. Dans une seconde partie du manuscrit, nous proposons également une méthode pour caractériser les rétines saines ou anormales. Certaines images contiennent de la néovascula- risation. La caractérisation des vaisseaux en bonne santé ou anormale constitue une étape essentielle pour le développement d’un système d’aide au diagnostic. En plus des défis que posent les petits vaisseaux sains, les néovaisseaux démontrent eux un degré de complexité encore plus élevé. Ceux-ci forment en effet des réseaux de vaisseaux à la morphologie com- plexe et inhabituelle, souvent minces et à fortes courbures. Les travaux existants se limitent viii à l’utilisation de caractéristiques de premier ordre extraites des petits vaisseaux segmentés. Notre contribution est d’utiliser le vote tensoriel pour isoler les jonctions vasculaires et d’uti- liser ces jonctions comme points d’intérêts. Nous utilisons ensuite une statistique spatiale de second ordre calculée sur les jonctions pour caractériser les vaisseaux comme étant sains ou pathologiques. Notre méthode améliore la sensibilité de la caractérisation de 9.09% par rapport à une méthode de l’état de l’art. La méthode développée s’est révélée efficace pour la segmentation des vaisseaux réti- niens. Des tenseurs d’ordre supérieur ainsi que la mise en œuvre d’un vote par tenseur via un filtrage orientable pourraient être étudiés pour réduire davantage le temps d’exécution et résoudre les défis encore présents au niveau des jonctions vasculaires. De plus, la caractéri- sation pourrait être améliorée pour la détection de la rétinopathie proliférative en utilisant un apprentissage supervisé incluant des cas de rétinopathie diabétique non proliférative ou d’autres pathologies. Finalement, l’incorporation des méthodes proposées dans des systèmes d’aide au diagnostic pourrait favoriser le dépistage régulier pour une détection précoce des rétinopathies et d’autres pathologies oculaires dans le but de réduire la cessité au sein de la population.----------ABSTRACT As an easily accessible site for the direct observation of the circulation system, human retina can offer a unique insight into diseases development or outcome. Retinal vessels are repre- sentative of the general condition of the whole systematic circulation, and thus can act as a "window" to the status of the vascular network in the whole body. Each complication on the retina can have an adverse impact on the patient’s sight. In this direction, small vessels’ relevance is very high as they are among the first anatomical structures that get affected as diseases progress. Moreover, changes in the small vessels’ state, appearance, morphology, functionality, or even growth indicate the severity of the diseases. This thesis will focus on the retinal lesions due to diabetes, a serious metabolic disease affecting millions of people around the world. This disorder disturbs the natural blood glucose levels causing various pathophysiological changes in different systems across the human body. Diabetic retinopathy is the medical term that describes the condition when the fundus and the retinal vessels are affected by diabetes. As in other diseases, small vessels play a crucial role in the onset, the development, and the outcome of the retinopathy. More importantly, at the latest stage, new small vessels, or neovascularizations, growth constitutes a factor of significant risk for blindness. Therefore, there is a need to detect all the changes that occur in the small retinal vessels with the aim of characterizing the vessels to healthy or abnormal. The characterization, in turn, can facilitate the detection of a specific retinopathy locally, like the sight-threatening proliferative diabetic retinopathy. Segmentation techniques can automatically isolate important anatomical structures like the vessels, and provide this information to the physician to assist him in the final decision. In comprehensive systems for the automatization of DR detection, small vessels role is significant as missing them early in a CAD pipeline might lead to an increase in the false positive rate of red lesions in subsequent steps. So far, the efforts have been concentrated mostly on the accurate localization of the medium range vessels. In contrast, the existing models are weak in case of the small vessels. The required generalization to adapt an existing model does not allow the approaches to be flexible, yet robust to compensate for the increased variability in the appearance as well as the interference with the background. So far, the current template models (matched filtering, line detection, and morphological processing) assume a general shape for the vessels that is not enough to approximate the narrow, curved, characteristics of the small vessels. Additionally, due to the weak contrast in the small vessel regions, the current segmentation and the tracking methods produce fragmented or discontinued results. Alternatively, the small vessel segmentation can be accomplished at the expense of x background noise magnification, in the case of using thresholding or the image derivatives methods. Furthermore, the proposed deformable models are not able to propagate a contour to the full extent of the vasculature in order to enclose all the small vessels. The deformable model external forces are ineffective to compensate for the low contrast, the low width, the high variability in the small vessel appearance, as well as the discontinuities. Internal forces, also, are not able to impose a global shape constraint to the contour that could be able to approximate the variability in the appearance of the vasculature in different categories of vessels. Finally, machine learning approaches require the training of a classifier on a labelled set. Those sets are difficult to be obtained, especially in the case of the smallest vessels. In the case of the unsupervised methods, the user has to predefine the number of clusters and perform an effective initialization of the cluster centers in order to converge to the global minimum. This dissertation expanded the previous research work and provides a new segmentation method for the smallest retinal vessels. Multi-scale line detection (MSLD) is a recent method that demonstrates good segmentation performance in the retinal images, while tensor voting is a method first proposed for reconnecting pixels. For the first time, we combined the line detection with the tensor voting framework. The application of the line detectors has been proved an effective way to segment medium-sized vessels. Additionally, perceptual organization approaches like tensor voting, demonstrate increased robustness by combining information coming from the neighborhood in a hierarchical way. Tensor voting is closer than standard models to the way human perception functions. As we show, it is a more powerful tool to segment small vessels than the existing methods. This specific combination allows us to overcome the apparent fragmentation challenge of the template methods at the smallest vessels. Moreover, we thresholded the line detection response adaptively to compensate for non-uniform images. We also combined the two individual methods in a multi-scale scheme in order to reconnect vessels at variable distances. Finally, we reconstructed the vessels from their extracted centerlines based on pixel painting as complete geometric information is required to be able to utilize the segmentation in a CAD system. The segmentation was validated on a high-resolution fundus image database that in- cludes diabetic retinopathy images of varying stages, using standard discrepancy as well as perceptual-based measures. When only the smallest vessels are considered, the improve- ments in the sensitivity rate for the database against the standard multi-scale line detection method is 6.47%. For the perceptual-based measure, the improvement is 7.8% against the basic method. The second objective of the thesis was to implement a method for the characterization of isolated retinal areas into healthy or abnormal cases. Some of the original images, from which xi these patches are extracted, contain neovascularizations. Investigation of image features for the vessels characterization to healthy or abnormal constitutes an essential step in the direction of developing CAD system for the automatization of DR screening. Given that the amount of data will significantly increase under CAD systems, the focus on this category of vessels can facilitate the referral of sight-threatening cases to early treatment. In addition to the challenges that small healthy vessels pose, neovessels demonstrate an even higher degree of complexity as they form networks of convolved, twisted, looped thin vessels. The existing work is limited to the use of first-order characteristics extracted from the small segmented vessels that limits the study of patterns. Our contribution is in using the tensor voting framework to isolate the retinal vascular junctions and in turn using those junctions as points of interests. Second, we exploited second-order statistics computed on the junction spatial distribution to characterize the vessels as healthy or neovascularizations. In fact, the second-order spatial statistics extracted from the junction distribution are combined with widely used features to improve the characterization sensitivity by 9.09% over the state of art. The developed method proved effective for the segmentation of the retinal vessels. Higher order tensors along with the implementation of tensor voting via steerable filtering could be employed to further reduce the execution time, and resolve the challenges at vascular junctions. Moreover, the characterization could be advanced to the detection of prolifera- tive retinopathy by extending the supervised learning to include non-proliferative diabetic retinopathy cases or other pathologies. Ultimately, the incorporation of the methods into CAD systems could facilitate screening for the effective reduction of the vision-threatening diabetic retinopathy rates, or the early detection of other than ocular pathologies

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research