Human Blastocyst\u27s Zona Pellucida Segmentation via Boosting Ensemble of Complementary Learning

Characteristics of Zona Pellucida (ZP), particularly its thickness, is a key indicator of human blastocyst (day-5embryo) quality. Therefore, ZP segmentation is an important step towards achieving automatic embryo qualityassessment. In this paper, a novel approach based on boosting ensemble of hybrid complementary learning isproposed to segment Zona Pellucida in human blastocyst images. First, an inner-ZP localization method isproposed to separate the ZP from the heavily textured area inside a blastocyst. Then, a deep Hierarchical NeuralNetwork (HiNN) is proposed to segment ZP area. The hierarchical nature of the proposed network enableslearning features with respect to their spatial location in the embryo. Finally, a Self-supervised Image-SpecificRefinement (SISR) strategy is proposed as a complementary step to boost the performance. The proposed systemis a hybrid approach in the sense that the HiNN learns the intra-correlation among images, while the SISR takesinto account the inter-correlation within the query image. Experimental results confirm that the proposed method is capable of identifying ZP area with average Precision, Recall, Accuracy and Jaccard Index of 85.2%, 92.0%, 95.6% and 78.1%, respectively. The proposed HiNN system outperforms state of the art by 4.9% in Precision, 11.2% in Recall, 3.6% in Accuracy and 10.7% in Jaccard Index

Computer-aided diagnosis of gynaecological abnormality using B-mode ultrasound images

Ultrasound scan is one of the most reliable imaging for detecting/diagnosing of gynaecological abnormalities. Ultrasound imaging is widely used during pregnancy and has become central in the management of the problems of early pregnancy, particularly in miscarriage diagnosis. Also ultrasound is considered as the most important imaging modality in the evaluation of different types of ovarian tumours. The early detection of ovarian carcinoma and miscarriage continues to be a challenging task. It mostly relies on manual examination, interpretation by gynaecologists, of the ultrasound scan images that may use morphology features extracted from the region of interest. Diagnosis depends on using certain scoring systems that have been devised over a long time. The manual diagnostic process involves multiple subjective decisions, with increased inter- and intra-observer variations which may lead to serious errors and health implications. This thesis is devoted to developing computer-based tools that use ultrasound scan images for automatic classification of Ovarian Tumours (Benign or Malignant) and automatic detection of Miscarriage cases at early stages of pregnancy. Our intended computational tools are meant to help gynaecologists to improve accuracy of their diagnostic decisions, while serving as a tool for training radiology students/trainees on diagnosing gynaecological abnormalities. Ultimately, it is hoped that the developed techniques can be integrated into a specialised gynaecology Decision Support System. Our approach is to deal with this problem by adopting a standard image-based pattern recognition research framework that involve the extraction of appropriate feature vector modelling of the investigated tumours, select appropriate classifiers, and test the performance of such schemes using sufficiently large and relevant datasets of ultrasound scan images. We aim to complement the automation of certain parameters that gynaecologist experts and radiologists manually determine, by image-content information attributes that may not be directly accessible without advanced image transformations. This is motivated by, and benefit from, advances in computer vision that led the emergence of a variety of image processing/analysis techniques together with recent advances in data mining and machine learning technologies. An expert observer makes a diagnostic decision with a level of certainty, and if not entirely certain about their diagnostic decisions then often other experts’ opinions are sought and may be essential for diagnosing difficult “Inconclusive cases”. Here we define a quantitative measure of confidence in decisions made by automatic diagnostic schemes, independent of accuracy of decision. In the rest of the thesis, we report on the development of a variety of innovative diagnostic schemes and demonstrate their performances using extensive experimental work. The following is a summary of the main contributions made in this thesis. 1. Using a combination of spatial domain filters and operations as pre-processing procedures to enhance ultrasound images for both applications, namely miscarriage identification and ovarian tumour diagnosis. We show that the Non-local means filter is effective in reducing speckle noise from ultrasound images, and together with other filters we succeed in enhancing the inner border of malignant tumours and reliably segmenting the gestational sac. 2. Developing reliable automated procedures to extract several types of features to model gestational sac dimensional measurements, few of which are manually determined by radiologist and used by gynaecologists to identify miscarriage cases. We demonstrate that the corresponding automatic diagnostic schemes yield excellent accuracy when classified by the k-Nearest Neighbours. 3. Developing several local as well as global image-texture based features in the spatial as well as the frequency domains. The spatial domain features include the local versions of image histograms, first order statistical features and versions of local binary patterns. From the frequency domain, we propose a novel set of Fast Fourier Geometrical Features that encapsulates the image texture information that depends on all image pixel values. We demonstrate that each of these features define Ovarian Tumour diagnostic scheme that have relatively high power of discriminating Benign from Malignant tumours when classified by Support Vector Machine. We show that the Fast Fourier Geometrical Features are the best performing scheme achieving more than 85% accuracy. 4. Introducing a simple measure of confidence to quantify the goodness of the automatic diagnostic decision, regardless of decision accuracy, to emulate real life medical diagnostics. Experimental work in this theis demonstrate a strong link between this measure and accuracy rate, so that low level of confidence could raise an alarm. 5. Conducting sufficiently intensive investigations of fusion models of multi-feature schemes at different level. We show that feature level fusion yields degraded performance compared to all its single components, while score level fusion results in improved results and decision level fusion of three sets of features using majority rule is slightly less successful. Using the measure of confidence is useful in resolving conflicts when two sets of features are fused at the decision level. This leads to the emergence of a Not Sure decision which is common in medical practice. Considering the Not Sure label is a good practice and an incentive to conduct more tests, rather than misclassification, which leads to significantly improved accuracy. The thesis concludes with an intensive discussion on future work that would go beyond improving performance of the developed scheme to deal with the corresponding multi-class diagnostics essential for a comprehensive gynaecology Decision Support System tool as the ultimate goal

Tracking-by-Assignment as a Probabilistic Graphical Model with Applications in Developmental Biology

This thesis presents a novel approach for tracking a varying number of divisible objects with similar appearance in the presence of a non-negligible number of false positive detections (more than 10%). It is applied to the reconstruction of cell lineages in developing zebrafish and fruit fly embryos from 3d time-lapse record- ings. The model takes the form of a chain graph—a mixed directed-undirected probabilistic graphical model—and a tracking is obtained simultaneously over all time slices from the maximum a-posteriori configuration. The tracking model is used as the second step in a two-step pipeline to produce digital embryos—maps of cell nuclei in an embryo and their ancestral fate; the first step being the segmentation of the fluorescently-stained cell nuclei in light sheet microscopy images. The pipeline is implemented as a software with an intuitive graphical user interface. It is the first freely available program of its kind and makes the presented methods accessible to a broad audience of users from the life sciences

Multimodal Biomedical Data Visualization: Enhancing Network, Clinical, and Image Data Depiction

In this dissertation, we present visual analytics tools for several biomedical applications. Our research spans three types of biomedical data: reaction networks, longitudinal multidimensional clinical data, and biomedical images. For each data type, we present intuitive visual representations and efficient data exploration methods to facilitate visual knowledge discovery. Rule-based simulation has been used for studying complex protein interactions. In a rule-based model, the relationships of interacting proteins can be represented as a network. Nevertheless, understanding and validating the intended behaviors in large network models are ineffective and error prone. We have developed a tool that first shows a network overview with concise visual representations and then shows relevant rule-specific details on demand. This strategy significantly improves visualization comprehensibility and disentangles the complex protein-protein relationships by showing them selectively alongside the global context of the network. Next, we present a tool for analyzing longitudinal multidimensional clinical datasets, that we developed for understanding Parkinson's disease progression. Detecting patterns involving multiple time-varying variables is especially challenging for clinical data. Conventional computational techniques, such as cluster analysis and dimension reduction, do not always generate interpretable, actionable results. Using our tool, users can select and compare patient subgroups by filtering patients with multiple symptoms simultaneously and interactively. Unlike conventional visualizations that use local features, many targets in biomedical images are characterized by high-level features. We present our research characterizing such high-level features through multiscale texture segmentation and deep-learning strategies. First, we present an efficient hierarchical texture segmentation approach that scales up well to gigapixel images to colorize electron microscopy (EM) images. This enhances visual comprehensibility of gigapixel EM images across a wide range of scales. Second, we use convolutional neural networks (CNNs) to automatically derive high-level features that distinguish cell states in live-cell imagery and voxel types in 3D EM volumes. In addition, we present a CNN-based 3D segmentation method for biomedical volume datasets with limited training samples. We use factorized convolutions and feature-level augmentations to improve model generalization and avoid overfitting

Mapping the Dynamic Protein Network of Dividing Cells in Space and Time

Live cell imaging is a powerful tool for studying the distribution and dynamics of proteins. However, due to the difficulties in absolute quantification and standardization of data obtained from individual cells, it has not been used to map large sets of proteins that carry out dynamic cellular functions. Cell division is a good example of this challenge for an essential cellular function, as rapid changes in protein localization and protein interactions result in dramatic changes to subcellular structures and cellular morphology, which in turn influence the behavior of the enclosed proteins. Here, I report an integrated experimental and computational pipeline to map the dynamic protein network of dividing human cells in space and time. Using 3D live confocal microscopy, I imaged human cell lines that stably expressed fluorescently tagged mitotic proteins throughout mitosis. To obtain the absolute quantities of protein abundance with high subcellular resolution over time, the microscopy pipeline was calibrated by fluorescence correlation spectroscopy (FCS). Cell and chromosome volumes were segmented as references of cellular context for temporal and spatial alignment based on fluorescent landmarks. Together with my colleague Julius Hossain, we computationally generated a canonical model of mitotic progression for both kinetics (“mitotic standard time”) and morphology (“mitotic standard space”) by averaging and kinetically and geometrically parametrizing many registered dividing cells. The resulting model enabled us to subdivide the mitotic process into 20 characteristic kinetic steps and integrate our complete proof of concept dataset of 13 mitotic proteins imaged in over 300 dividing cells, represented as the 3D protein localization probability of each protein over time. To measure localization similarities between different proteins and make predictions about their dynamic interactions, the integrated data was then mined using supervised as well as unsupervised machine learning. The power of this approach was demonstrated by our ability to automatically identify the major subcellular localizations of all proteins in the dataset and quantify protein fluxes between subcellular compartments and structures. Due to the quantitative nature of our imaging data, we were able to estimate the abundance of each protein in mitotic structures and complexes such as kinetochores, centrosomes, and the midbody, and determine the order and kinetics of their formation and disassembly. The integrated computational and experimental method I present in my thesis is generic and scalable and makes many dynamic cellular processes amenable to dynamic protein network analysis even for large numbers of components. The pipeline provides a powerful instrument for analyzing large sets of quantitative live imaging data of fluorescently tagged proteins. It allows the systematic mapping and prediction of dynamic protein networks that drive complex cellular processes such as mitosis, thus promoting our understanding of the mechanisms by which many molecules together achieve spatio-temporal regulation

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