Incorporating a Local Binary Fitting Model into a Maximum Regional Difference Model for Extracting Microscopic Information under Complex Conditions

This paper presents a novel region-based method for extracting useful information from microscopic images under complex conditions. It is especially used for blood cell segmentation and statistical analysis. The active model detects several inner and outer contours of an object from its background. The method incorporates a local binary fitting model into a maximum regional difference model. It utilizes both local and global intensity information as the driving forces of the contour model on the principle of the largest regional difference. The local and global fitting forces ensure that local dissimilarities can be captured and globally different areas can be segmented, respectively. By combining the advantages of local and global information, the motion of the contour is driven by the mixed fitting force, which is composed of the local and global fitting term in the energy function. Experiments are carried out in the laboratory, and results show that the novel model can yield good performances for microscopic image analysis

β-CATENIN REGULATION OF ADULT SKELETAL MUSCLE PLASTICITY

Adult skeletal muscle is highly plastic and responds readily to environmental stimuli. One of the most commonly utilized methods to study skeletal muscle adaptations is immunofluorescence microscopy. By analyzing images of adult muscle cells, also known as myofibers, one can quantify changes in skeletal muscle structure and function (e.g. hypertrophy and fiber type). Skeletal muscle samples are typically cut in transverse or cross sections, and antibodies against sarcolemmal or basal lamina proteins are used to label the myofiber boundaries. The quantification of hundreds to thousands of myofibers per sample is accomplished either manually or semi-automatically using generalized pathology software, and such approaches become exceedingly tedious. In the first study, I developed MyoVision, a robust, fully automated software that is dedicated to skeletal muscle immunohistological image analysis. The software has been made freely available to muscle biologists to alleviate the burden of routine image analyses. To date, more than 60 technicians, students, postdoctoral fellows, faculty members, and others have requested this software. Using MyoVision, I was able to accurately quantify the effects of β-catenin knockout on myofiber hypertrophy. In the second study, I tested the hypothesis that myofiber hypertrophy requires β-catenin to activate c-myc transcription and promote ribosome biogenesis. Recent evidence in both mice and human suggests a close association between ribosome biogenesis and skeletal muscle hypertrophy. Using an inducible mouse model of skeletal myofiber-specific genetic knockout, I obtained evidence that β-catenin is important for myofiber hypertrophy, although its role in ribosome biogenesis appears to be dispensable for mechanical overload induced muscle growth. Instead, β-catenin may be necessary for promoting the translation of growth related genes through activation of ribosomal protein S6. Unexpectedly, we detected a novel, enhancing effect of myofiber β-catenin knockout on the resident muscle stem cells, or satellite cells. In the absence of myofiber β-catenin, satellite cells activate and proliferate earlier in response to mechanical overload. Consistent with the role of satellite cells in muscle repair, the enhanced recruitment of satellite cells led to a significantly improved regeneration response after chemical injury. The novelty of these findings resides in the fact that the genetic perturbation was extrinsic to the satellite cells, and this is even more surprising because the current literature focuses heavily on intrinsic mechanisms within satellite cells. As such, this model of myofiber β-catenin knockout may significantly contribute to better understanding of the mechanisms of satellite cell priming, with implications for regenerative medicine

Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey

Electron microscopy (EM) enables high-resolution imaging of tissues and cells based on 2D and 3D imaging techniques. Due to the laborious and time-consuming nature of manual segmentation of large-scale EM datasets, automated segmentation approaches are crucial. This review focuses on the progress of deep learning-based segmentation techniques in large-scale cellular EM throughout the last six years, during which significant progress has been made in both semantic and instance segmentation. A detailed account is given for the key datasets that contributed to the proliferation of deep learning in 2D and 3D EM segmentation. The review covers supervised, unsupervised, and self-supervised learning methods and examines how these algorithms were adapted to the task of segmenting cellular and sub-cellular structures in EM images. The special challenges posed by such images, like heterogeneity and spatial complexity, and the network architectures that overcame some of them are described. Moreover, an overview of the evaluation measures used to benchmark EM datasets in various segmentation tasks is provided. Finally, an outlook of current trends and future prospects of EM segmentation is given, especially with large-scale models and unlabeled images to learn generic features across EM datasets

Study of the role of plant nuclear envelope and lamina-like components in nuclear and chromatin organisation using 3D imaging

The linker of nucleoskeleton and cytoskeleton (LINC) complex is an evolutionarily well-conserved protein bridge connecting the cytoplasmic and nuclear compartments across the nuclear membrane. While recent data supports its function in nuclear morphology and meiosis, its implication for chromatin organisation has been less studied in plants. The fi aim of this work was to develop NucleusJ a simple and user-friendly ImageJ plugin dedicated to the characterisation of nuclear morphol- ogy and chromatin organisation in 3D. NucleusJ quantifies 15 parameters including shape and size of nuclei as well as intra-nuclear objects and their position within the nucleus. A step-by-step documentation is available for self-training, together with data sets of nuclei with diff t nuclear organisation. Several improvements are ongoing to release a new version of this plugin. In a second part of this work, 3D imaging methods have been used to investigate nuclear morphology and chromatin organisation in interphase nuclei of the plant model Arabidopsis thaliana in which heterochromatin domains cluster in conspicuous chromatin regions called chromo- centres. Chromocentres form a repressive chromatin environment contributing to the transcriptional silencing of repeated sequences a general mechanism needed for genome stability. Quantitative measurements of 3D position of chromocentres in the nucleus indicate that most chromocentres are situated in close proximity to the periphery of the nucleus but that this distance can be altered according to nuclear volume or in specific mutants affecting the LINC complex. Finally, the LINC com- plex is proposed to contribute at the proper chromatin organisation and positioning since its alteration is associated with the release of transcriptional silencing as well as decompaction of heterochromatic sequences. The last part of this work takes ad- vantage of available genomic sequences and RNA-seq data to explore the evolution of NE proteins in plants and propose a minimal requirement to built the simplest functional NE. Altogether, work achieved in this thesis associate genetics, molecular biology, bioinformatics and imaging to better understand the contribution of the nuclear envelope in nuclear morphology and chromatin organisation and suggests the functional implication of the LINC complex in these processes

Endosome detection in cell images

Master'sMASTER OF SCIENC

Automated Correlative Light and Electron Microscopy using FIB-SEM as a tool to screen for ultrastructural phenotypes

In Correlative Light and Electron Microscopy (CLEM), two imaging modalities are combined to take advantage of the localization capabilities of light microscopy (LM) to guide the capture of high-resolution details in the electron microscope (EM). However, traditional approaches have proven to be very laborious, thus yielding a too low throughput for quantitative or exploratory studies of populations. Recently, in the electron microscopy field, FIB-SEM (Focused Ion Beam -Scanning Electron Microscope) tomography has emerged as a flexible method that enables semi-automated 3D volume acquisitions. During my thesis, I developed CLEMSite, a tool that takes advantage of the semi-automation and scanning capabilities of the FIB-SEM to automatically acquire volumes of adherent cultured cells. CLEMSite is a combination of computer vision and machine learning applications with a library for controlling the microscope ( product from a collaboration with Carl Zeiss GmbH and Fibics Inc.). Thanks to this, the microscope was able to automatically track, find and acquire cell regions previously identified in the light microscope. More specifically, two main modules were implemented. First, a correlation module was designed to detect and record reference points from a grid pattern present on the culture substrate in both modalities (LM and EM). Second, I designed a module that retrieves the regions of interest in the FIB-SEM and that drives the acquisition of image stacks between different targets in an unattended fashion. The automated CLEM approach is demonstrated on a project where 3D EM volumes are examined upon multiple siRNA treatments for knocking down genes involved in the morphogenesis of the Golgi apparatus. Additionally, the power of CLEM approaches using FIB-SEM is demonstrated with the detailed structural analysis of two events: the breakage of the nuclear envelope within constricted cells and an intriguing catastrophic DNA Damage Response in binucleated cells. Our results demonstrate that executing high throughput volume acquisition in electron microscopy is possible and that EM can provide incredible insights to guide new biological discoveries

Computerized cancer malignancy grading of fine needle aspirates

According to the World Health Organization, breast cancer is a leading cause of death among middle-aged women. Precise diagnosis and correct treatment significantly reduces the high number of deaths caused by breast cancer. Being successful in the treatment strictly relies on the diagnosis. Specifically, the accuracy of the diagnosis and the stage at which a cancer was diagnosed. Precise and early diagnosis has a major impact on the survival rate, which indicates how many patients will live after the treatment. For many years researchers in medical and computer science fields have been working together to find the approach for precise diagnosis. For this thesis, precise diagnosis means finding a cancer at as early a stage as possible by developing new computer aided diagnostic tools. These tools differ depending on the type of cancer and the type of the examination that is used for diagnosis. This work concentrates on cytological images of breast cancer that are produced during fine needle aspiration biopsy examination. This kind of examination allows pathologists to estimate the malignancy of the cancer with very high accuracy. Malignancy estimation is very important when assessing a patients survival rate and the type of treatment. To achieve precise malignancy estimation, a classification framework is presented. This framework is able to classify breast cancer malignancy into two malignancy classes and is based on features calculated according to the Bloom-Richardson grading scheme. This scheme is commonly used by pathologists when grading breast cancer tissue. In Bloom-Richardson scheme two types of features are assessed depending on the magnification. Low magnification images are used for examining the dispersion of the cells in the image while the high magnification images are used for precise analysis of the cells' nuclear features. In this thesis, different types of segmentation algorithms were compared to estimate the algorithm that allows for relatively fast and accurate nuclear segmentation. Based on that segmentation a set of 34 features was extracted for further malignancy classification. For classification purposes 6 different classifiers were compared. From all of the tests a set of the best preforming features were chosen. The presented system is able to classify images of fine needle aspiration biopsy slides with high accurac

Automatic Segmentation of Cells of Different Types in Fluorescence Microscopy Images

Recognition of different cell compartments, types of cells, and their interactions is a critical aspect of quantitative cell biology. This provides a valuable insight for understanding cellular and subcellular interactions and mechanisms of biological processes, such as cancer cell dissemination, organ development and wound healing. Quantitative analysis of cell images is also the mainstay of numerous clinical diagnostic and grading procedures, for example in cancer, immunological, infectious, heart and lung disease. Computer automation of cellular biological samples quantification requires segmenting different cellular and sub-cellular structures in microscopy images. However, automating this problem has proven to be non-trivial, and requires solving multi-class image segmentation tasks that are challenging owing to the high similarity of objects from different classes and irregularly shaped structures. This thesis focuses on the development and application of probabilistic graphical models to multi-class cell segmentation. Graphical models can improve the segmentation accuracy by their ability to exploit prior knowledge and model inter-class dependencies. Directed acyclic graphs, such as trees have been widely used to model top-down statistical dependencies as a prior for improved image segmentation. However, using trees, a few inter-class constraints can be captured. To overcome this limitation, polytree graphical models are proposed in this thesis that capture label proximity relations more naturally compared to tree-based approaches. Polytrees can effectively impose the prior knowledge on the inclusion of different classes by capturing both same-level and across-level dependencies. A novel recursive mechanism based on two-pass message passing is developed to efficiently calculate closed form posteriors of graph nodes on polytrees. Furthermore, since an accurate and sufficiently large ground truth is not always available for training segmentation algorithms, a weakly supervised framework is developed to employ polytrees for multi-class segmentation that reduces the need for training with the aid of modeling the prior knowledge during segmentation. Generating a hierarchical graph for the superpixels in the image, labels of nodes are inferred through a novel efficient message-passing algorithm and the model parameters are optimized with Expectation Maximization (EM). Results of evaluation on the segmentation of simulated data and multiple publicly available fluorescence microscopy datasets indicate the outperformance of the proposed method compared to state-of-the-art. The proposed method has also been assessed in predicting the possible segmentation error and has been shown to outperform trees. This can pave the way to calculate uncertainty measures on the resulting segmentation and guide subsequent segmentation refinement, which can be useful in the development of an interactive segmentation framework