Modellistica computazionale di cardiomiociti derivati da cellule staminali umane - Texture descriptor per l'elaborazione di immagini biologiche

This thesis investigates two distinct research topics. The main topic (Part I) is the computational modelling of cardiomyocytes derived from human stem cells, both embryonic (hESC-CM) and induced-pluripotent (hiPSC-CM). The aim of this research line lies in developing models of the electrophysiology of hESC-CM and hiPSC-CM in order to integrate the available experimental data and getting in-silico models to be used for studying/making new hypotheses/planning experiments on aspects not fully understood yet, such as the maturation process, the functionality of the Ca2+ hangling or why the hESC-CM/hiPSC-CM action potentials (APs) show some differences with respect to APs from adult cardiomyocytes. Chapter I.1 introduces the main concepts about hESC-CMs/hiPSC-CMs, the cardiac AP, and computational modelling. Chapter I.2 presents the hESC-CM AP model, able to simulate the maturation process through two developmental stages, Early and Late, based on experimental and literature data. Chapter I.3 describes the hiPSC-CM AP model, able to simulate the ventricular-like and atrial-like phenotypes. This model was used to assess which currents are responsible for the differences between the ventricular-like AP and the adult ventricular AP. The secondary topic (Part II) consists in the study of texture descriptors for biological image processing. Chapter II.1 provides an overview on important texture descriptors such as Local Binary Pattern or Local Phase Quantization. Moreover the non-binary coding and the multi-threshold approach are here introduced. Chapter II.2 shows that the non-binary coding and the multi-threshold approach improve the classification performance of cellular/sub-cellular part images, taken from six datasets. Chapter II.3 describes the case study of the classification of indirect immunofluorescence images of HEp2 cells, used for the antinuclear antibody clinical test. Finally the general conclusions are reported.Questa tesi indaga due distinti temi di ricerca. Il tema principale (Parte I) è la modellistica computazionale di cardiomiociti derivati da cellule staminali umane, sia embrionali (hESC-CM) che pluripotenti-indotte (hiPSC-CM). Lo scopo di questa parte consiste nello sviluppare modelli elettrofisiologici di hESC-CM e hiPSC-CM al fine di integrare i dati sperimentali disponibili ed ottenere modelli in-silico utilizzabili per studiare/produrre ipotesi/pianificare esperimenti su aspetti ancora poco chiari come il processo di maturazione, la funzionalità del Ca2+ handling e le cause per cui i potenziali d'azione(PA)di hESC-CM/hiPSC-CM mostrino alcune differenze rispetto ai PA di cardiomiociti adulti. Il capitolo I.1 introduce i concetti base su hESC-CM/hiPSC-CM, PA cardiaco e modellistica computazionale. Il capitolo I.2 presenta il modello di PA di hESC-CM in grado di riprodurre il processo di maturazione attraverso due stadi di sviluppo, Early e Late, basato su esperimenti e dati di letteratura. Il capitolo I.3 descrive il modello di PA di hiPSC-CM, in grado di riprodurre i fenotipi ventricular-like ed atrial-like. Questo modello è stato utilizzato per valutare quali correnti siano le principali responsabili delle differenze tra il PA ventricular-like e quello di cardiomiociti ventricolari adulti. Il tema secondario (Parte II) consiste nello studio di texture descriptor per la classificazione di immagini biologiche. Nel capitolo II.1 viene fornita una panoramica dei principali texture descriptor come Local Binary Pattern e Local Phase Quantization. Inoltre viene presentato il concetto di codifica non-binaria e approccio multi-threshold. Il Capitolo II.2 mostra che l'utilizzo della codifica non-binaria e dell'approccio multi-threshold portano ad un incremento delle performance di classificazione su sei dataset di immagini cellulari o di parti subcellulari. Il capitolo II.3 presenta un caso di studio di classificazione di immagini di immunofluorescenza indiretta su cellule HEp2, utilizzate in clinica per il test degli anticorpi antinucleo. Infine vengono riportate le conclusioni generali

New algorithms for the analysis of live-cell images acquired in phase contrast microscopy

La détection et la caractérisation automatisée des cellules constituent un enjeu important dans de nombreux domaines de recherche tels que la cicatrisation, le développement de l'embryon et des cellules souches, l’immunologie, l’oncologie, l'ingénierie tissulaire et la découverte de nouveaux médicaments. Étudier le comportement cellulaire in vitro par imagerie des cellules vivantes et par le criblage à haut débit implique des milliers d'images et de vastes quantités de données. Des outils d'analyse automatisés reposant sur la vision numérique et les méthodes non-intrusives telles que la microscopie à contraste de phase (PCM) sont nécessaires. Comme les images PCM sont difficiles à analyser en raison du halo lumineux entourant les cellules et de la difficulté à distinguer les cellules individuelles, le but de ce projet était de développer des algorithmes de traitement d'image PCM dans Matlab® afin d’en tirer de l’information reliée à la morphologie cellulaire de manière automatisée. Pour développer ces algorithmes, des séries d’images de myoblastes acquises en PCM ont été générées, en faisant croître les cellules dans un milieu avec sérum bovin (SSM) ou dans un milieu sans sérum (SFM) sur plusieurs passages. La surface recouverte par les cellules a été estimée en utilisant un filtre de plage de valeurs, un seuil et une taille minimale de coupe afin d'examiner la cinétique de croissance cellulaire. Les résultats ont montré que les cellules avaient des taux de croissance similaires pour les deux milieux de culture, mais que celui-ci diminue de façon linéaire avec le nombre de passages. La méthode de transformée par ondelette continue combinée à l’analyse d'image multivariée (UWT-MIA) a été élaborée afin d’estimer la distribution de caractéristiques morphologiques des cellules (axe majeur, axe mineur, orientation et rondeur). Une analyse multivariée réalisée sur l’ensemble de la base de données (environ 1 million d’images PCM) a montré d'une manière quantitative que les myoblastes cultivés dans le milieu SFM étaient plus allongés et plus petits que ceux cultivés dans le milieu SSM. Les algorithmes développés grâce à ce projet pourraient être utilisés sur d'autres phénotypes cellulaires pour des applications de criblage à haut débit et de contrôle de cultures cellulaires.Automated cell detection and characterization is important in many research fields such as wound healing, embryo development, immune system studies, cancer research, parasite spreading, tissue engineering, stem cell research and drug research and testing. Studying in vitro cellular behavior via live-cell imaging and high-throughput screening involves thousands of images and vast amounts of data, and automated analysis tools relying on machine vision methods and non-intrusive methods such as phase contrast microscopy (PCM) are a necessity. However, there are still some challenges to overcome, since PCM images are difficult to analyze because of the bright halo surrounding the cells and blurry cell-cell boundaries when they are touching. The goal of this project was to develop image processing algorithms to analyze PCM images in an automated fashion, capable of processing large datasets of images to extract information related to cellular viability and morphology. To develop these algorithms, a large dataset of myoblasts images acquired in live-cell imaging (in PCM) was created, growing the cells in either a serum-supplemented (SSM) or a serum-free (SFM) medium over several passages. As a result, algorithms capable of computing the cell-covered surface and cellular morphological features were programmed in Matlab®. The cell-covered surface was estimated using a range filter, a threshold and a minimum cut size in order to look at the cellular growth kinetics. Results showed that the cells were growing at similar paces for both media, but their growth rate was decreasing linearly with passage number. The undecimated wavelet transform multivariate image analysis (UWT-MIA) method was developed, and was used to estimate cellular morphological features distributions (major axis, minor axis, orientation and roundness distributions) on a very large PCM image dataset using the Gabor continuous wavelet transform. Multivariate data analysis performed on the whole database (around 1 million PCM images) showed in a quantitative manner that myoblasts grown in SFM were more elongated and smaller than cells grown in SSM. The algorithms developed through this project could be used in the future on other cellular phenotypes for high-throughput screening and cell culture control applications

Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and

The use of high-throughput microscopy in the characterisation of phenotypes associated with the Unfolded Protein Response in Saccharomyces cerevisiae

Proteins traversing the secretory pathway begin their passage in the endoplasmic reticulum (ER) where they must be correctly folded and processed to pass quality control measures. Complications with this process can result in the accumulation of misfolded proteins, commonly referred to as ER-stress, which has been associated with a number of diseases. The unfolded protein response (UPR) is the cell’s mechanism of dealing with ER-stress and is activated via the IRE1-HAC1 pathway in yeast. Ire1p is the ER-stress sensor and upon recognising misfolded proteins Ire1 oligomerises and forms local clusters. Activated Ire1p then splices out an inhibitory intron from the UPR specific transcription factor Hac1p which goes on to initiate downstream responses to alleviate ER-stress. Here we utilise high-throughput microscopy and UPR-specific GFP reporter systems to characterise the UPR in the yeast Saccharomyces cerevisiae. High-throughput microscopy and automated image analysis is increasingly being used as a screening tool for investigating genome-wide collections of yeast strains, including the yeast deletion mutant array and the yeast GFP collection. We describe the use of GFP labelled Ire1p to visualise cluster formation as a reporter for early UPR recognition of misfolded proteins, as well as a GFP controlled by a Hac1p responsive promoter to measure downstream UPR activation. These UPR-specific GFP reporter systems were used to screen a collection of non-essential gene deletion strains, identifying gene deletions that induce UPR activation and thus are likely to function in the early secretory pathway. This included well known components such as the ALG members of the glycosylation pathway and various ER chaperones such as LHS1 and SCJ1. Additionally this analysis revealed 44 previously uncharacterised genes, suggesting there are still processes related to the secretory pathway that are yet to be described. Moreover, by inducing ER-stress in this screening system we revealed genes required for the normal activation of the UPR including ribosomal/translation and chromatin/transcriptionally related genes, as well as various genes from throughout the secretory pathway. Furthermore, we screened a collection of ~4000 strains, each expressing a different GFP fusion protein, under ER-stress conditions to identify protein expression and localisation changes induced by the UPR. Comparison to UPR deficient Δhac1 cells uncovered a set of UPR specific targets including 26 novel UPR targets that had not been identified in previous studies measuring changes at the transcript level. As part of this work, we developed a dual red fluorescent protein system to label cells for automated image segmentation to enable single cell phenotype measurements. Here we describe the use of texture analysis as a means of increasing automation in the identification of phenotypic changes across the proteome. These novel techniques may be more widely applied to screening GFP collections to increase automation of image analysis, particularly as manual annotation of phenotypic changes is a major bottleneck in high-throughput screening. The results presented here from microscopy based screening compare well with other techniques in the literature, but also provide new information highlighting the synergistic effects of integrating high-throughput imaging into traditional screening methodologies

Applications of Medical Physics

Applications of Medical Physics” is a Special Issue of Applied Sciences that has collected original research manuscripts describing cutting-edge physics developments in medicine and their translational applications. Reviews providing updates on the latest progresses in this field are also included. The collection includes a total of 20 contributions by authors from 9 different countries, which cover several areas of medical physics, spanning from radiation therapy, nuclear medicine, radiology, dosimetry, radiation protection, and radiobiology

Unfamiliar facial identity registration and recognition performance enhancement

The work in this thesis aims at studying the problems related to the robustness of a face recognition system where specific attention is given to the issues of handling the image variation complexity and inherent limited Unique Characteristic Information (UCI) within the scope of unfamiliar identity recognition environment. These issues will be the main themes in developing a mutual understanding of extraction and classification tasking strategies and are carried out as a two interdependent but related blocks of research work. Naturally, the complexity of the image variation problem is built up from factors including the viewing geometry, illumination, occlusion and other kind of intrinsic and extrinsic image variation. Ideally, the recognition performance will be increased whenever the variation is reduced and/or the UCI is increased. However, the variation reduction on 2D facial images may result in loss of important clues or UCI data for a particular face alternatively increasing the UCI may also increase the image variation. To reduce the lost of information, while reducing or compensating the variation complexity, a hybrid technique is proposed in this thesis. The technique is derived from three conventional approaches for the variation compensation and feature extraction tasks. In this first research block, transformation, modelling and compensation approaches are combined to deal with the variation complexity. The ultimate aim of this combination is to represent (transformation) the UCI without losing the important features by modelling and discard (compensation) and reduce the level of the variation complexity of a given face image. Experimental results have shown that discarding a certain obvious variation will enhance the desired information rather than sceptical in losing the interested UCI. The modelling and compensation stages will benefit both variation reduction and UCI enhancement. Colour, gray level and edge image information are used to manipulate the UCI which involve the analysis on the skin colour, facial texture and features measurement respectively. The Derivative Linear Binary transformation (DLBT) technique is proposed for the features measurement consistency. Prior knowledge of input image with symmetrical properties, the informative region and consistency of some features will be fully utilized in preserving the UCI feature information. As a result, the similarity and dissimilarity representation for identity parameters or classes are obtained from the selected UCI representation which involves the derivative features size and distance measurement, facial texture and skin colour. These are mainly used to accommodate the strategy of unfamiliar identity classification in the second block of the research work. Since all faces share similar structure, classification technique should be able to increase the similarities within the class while increase the dissimilarity between the classes. Furthermore, a smaller class will result on less burden on the identification or recognition processes. The proposed method or collateral classification strategy of identity representation introduced in this thesis is by manipulating the availability of the collateral UCI for classifying the identity parameters of regional appearance, gender and age classes. In this regard, the registration of collateral UCI s have been made in such a way to collect more identity information. As a result, the performance of unfamiliar identity recognition positively is upgraded with respect to the special UCI for the class recognition and possibly with the small size of the class. The experiment was done using data from our developed database and open database comprising three different regional appearances, two different age groups and two different genders and is incorporated with pose and illumination image variations

Complex queries and complex data

With the widespread availability of wearable computers, equipped with sensors such as GPS or cameras, and with the ubiquitous presence of micro-blogging platforms, social media sites and digital marketplaces, data can be collected and shared on a massive scale. A necessary building block for taking advantage from this vast amount of information are efficient and effective similarity search algorithms that are able to find objects in a database which are similar to a query object. Due to the general applicability of similarity search over different data types and applications, the formalization of this concept and the development of strategies for evaluating similarity queries has evolved to an important field of research in the database community, spatio-temporal database community, and others, such as information retrieval and computer vision. This thesis concentrates on a special instance of similarity queries, namely k-Nearest Neighbor (kNN) Queries and their close relative, Reverse k-Nearest Neighbor (RkNN) Queries. As a first contribution we provide an in-depth analysis of the RkNN join. While the problem of reverse nearest neighbor queries has received a vast amount of research interest, the problem of performing such queries in a bulk has not seen an in-depth analysis so far. We first formalize the RkNN join, identifying its monochromatic and bichromatic versions and their self-join variants. After pinpointing the monochromatic RkNN join as an important and interesting instance, we develop solutions for this class, including a self-pruning and a mutual pruning algorithm. We then evaluate these algorithms extensively on a variety of synthetic and real datasets. From this starting point of similarity queries on certain data we shift our focus to uncertain data, addressing nearest neighbor queries in uncertain spatio-temporal databases. Starting from the traditional definition of nearest neighbor queries and a data model for uncertain spatio-temporal data, we develop efficient query mechanisms that consider temporal dependencies during query evaluation. We define intuitive query semantics, aiming not only at returning the objects closest to the query but also their probability of being a nearest neighbor. After theoretically evaluating these query predicates we develop efficient querying algorithms for the proposed query predicates. Given the findings of this research on nearest neighbor queries, we extend these results to reverse nearest neighbor queries. Finally we address the problem of querying large datasets containing set-based objects, namely image databases, where images are represented by (multi-)sets of vectors and additional metadata describing the position of features in the image. We aim at reducing the number of kNN queries performed during query processing and evaluate a modified pipeline that aims at optimizing the query accuracy at a small number of kNN queries. Additionally, as feature representations in object recognition are moving more and more from the real-valued domain to the binary domain, we evaluate efficient indexing techniques for binary feature vectors.Nicht nur durch die Verbreitung von tragbaren Computern, die mit einer Vielzahl von Sensoren wie GPS oder Kameras ausgestattet sind, sondern auch durch die breite Nutzung von Microblogging-Plattformen, Social-Media Websites und digitale Marktplätze wie Amazon und Ebay wird durch die User eine gigantische Menge an Daten veröffentlicht. Um aus diesen Daten einen Mehrwert erzeugen zu können bedarf es effizienter und effektiver Algorithmen zur Ähnlichkeitssuche, die zu einem gegebenen Anfrageobjekt ähnliche Objekte in einer Datenbank identifiziert. Durch die Allgemeinheit dieses Konzeptes der Ähnlichkeit über unterschiedliche Datentypen und Anwendungen hinweg hat sich die Ähnlichkeitssuche zu einem wichtigen Forschungsfeld, nicht nur im Datenbankumfeld oder im Bereich raum-zeitlicher Datenbanken, sondern auch in anderen Forschungsgebieten wie dem Information Retrieval oder dem Maschinellen Sehen entwickelt. In der vorliegenden Arbeit beschäftigen wir uns mit einem speziellen Anfrageprädikat im Bereich der Ähnlichkeitsanfragen, mit k-nächste Nachbarn (kNN) Anfragen und ihrem Verwandten, den Revers k-nächsten Nachbarn (RkNN) Anfragen. In einem ersten Beitrag analysieren wir den RkNN Join. Obwohl das Problem von reverse nächsten Nachbar Anfragen in den letzten Jahren eine breite Aufmerksamkeit in der Forschungsgemeinschaft erfahren hat, wurde das Problem eine Menge von RkNN Anfragen gleichzeitig auszuführen nicht ausreichend analysiert. Aus diesem Grund formalisieren wir das Problem des RkNN Joins mit seinen monochromatischen und bichromatischen Varianten. Wir identifizieren den monochromatischen RkNN Join als einen wichtigen und interessanten Fall und entwickeln entsprechende Anfragealgorithmen. In einer detaillierten Evaluation vergleichen wir die ausgearbeiteten Verfahren auf einer Vielzahl von synthetischen und realen Datensätzen. Nach diesem Kapitel über Ähnlichkeitssuche auf sicheren Daten konzentrieren wir uns auf unsichere Daten, speziell im Bereich raum-zeitlicher Datenbanken. Ausgehend von der traditionellen Definition von Nachbarschaftsanfragen und einem Datenmodell für unsichere raum-zeitliche Daten entwickeln wir effiziente Anfrageverfahren, die zeitliche Abhängigkeiten bei der Anfragebearbeitung beachten. Zu diesem Zweck definieren wir Anfrageprädikate die nicht nur die Objekte zurückzugeben, die dem Anfrageobjekt am nächsten sind, sondern auch die Wahrscheinlichkeit mit der sie ein nächster Nachbar sind. Wir evaluieren die definierten Anfrageprädikate theoretisch und entwickeln effiziente Anfragestrategien, die eine Anfragebearbeitung zu vertretbaren Laufzeiten gewährleisten. Ausgehend von den Ergebnissen für Nachbarschaftsanfragen erweitern wir unsere Ergebnisse auf Reverse Nachbarschaftsanfragen. Zuletzt behandeln wir das Problem der Anfragebearbeitung bei Mengen-basierten Objekten, die zum Beispiel in Bilddatenbanken Verwendung finden: Oft werden Bilder durch eine Menge von Merkmalsvektoren und zusätzliche Metadaten (zum Beispiel die Position der Merkmale im Bild) dargestellt. Wir evaluieren eine modifizierte Pipeline, die darauf abzielt, die Anfragegenauigkeit bei einer kleinen Anzahl an kNN-Anfragen zu maximieren. Da reellwertige Merkmalsvektoren im Bereich der Objekterkennung immer öfter durch Bitvektoren ersetzt werden, die sich durch einen geringeren Speicherplatzbedarf und höhere Laufzeiteffizienz auszeichnen, evaluieren wir außerdem Indexierungsverfahren für Binärvektoren

Novel feature selection methods for high dimensional data

[Resumen] La selección de características se define como el proceso de detectar las características relevantes y descartar las irrelevantes, con el objetivo de obtener un subconjunto de características más pequeño que describa adecuadamente el problema dado con una degradación mínima o incluso con una mejora del rendimiento. Con la llegada de los conjuntos de alta dimensión -tanto en muestras como en características-, se ha vuelto indispensable la identifícación adecuada de las características relevantes en escenarios del mundo real. En este contexto, los diferentes métodos disponibles se encuentran con un nuevo reto en cuanto a aplicabilidad y escalabilidad. Además, es necesario desarrollar nuevos métodos que tengan en cuenta estas particularidades de la alta dimensión. Esta tesis está dedicada a la investigación en selección de características y a su aplicación a datos reales de alta dimensión. La primera parte de este trabajo trata del análisis de los métodos de selección de características existentes, para comprobar su idoneidad frente a diferentes retos y para poder proporcionar nuevos resultados a los investigadores de selección de características. Para esto, se han aplicado las técnicas más populares a problemas reales, con el objetivo de obtener no sólo mejoras en rendimiento sino también para permitir su aplicación en tiempo real. Además de la eficiencia, la escalabilidad también es un aspecto crítico en aplicaciones de gran escala. La eficacia de los métodos de selección de características puede verse significativamente degradada, si no totalmente inaplicable, cuando el tamaño de los datos se incrementa continuamente. Por este motivo, la escalabilidad de los métodos de selección de características también debe ser analizada. Tras llevar a cabo un análisis en profundidad de los métodos de selección de características existentes, la segunda parte de esta tesis se centra en el desarrollo de nuevas técnicas. Debido a que la mayoría de métodos de selección existentes necesitan que los datos sean discretos, la primera aproximación propuesta consiste en la combinación de un discretizador, un filtro y un clasificador, obteniendo resultados prometedores en escenarios diferentes. En un intento de introducir diversidad, la segunda propuesta trata de usar un conjunto de filtros en lugar de uno sólo, con el objetivo de liberar al usuario de tener que decidir que técnica es la más adecuada para un problema dado. La tercera técnica propuesta en esta tesis no solo considera la relevancia de las características sino también su coste asociado -económico o en cuanto a tiempo de ejecución-, por lo que se presenta una metodología general para selección de características basada en coste. Por último, se proponen varias estrategias para distribuir y paralelizar la selección de características, ya que transformar un problema de gran escala en varios problemas de pequeña escala puede llevar a mejoras en el tiempo de procesado y, en algunas ocasiones, en precisión de clasificación