Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentation

In this paper, we improve upon earlier approaches to segmenting mitochondria in Electron Microscopy images by explicitly modeling the double membrane that encloses mitochondria, as well as using features that capture context over an extended neighborhood. We demonstrate that this results in both improved classification accuracy and reduced computational requirements for training

Scalable Unsupervised Domain Adaptation for Electron Microscopy

While Machine Learning algorithms are key to automating organelle segmentation in large EM stacks, they require annotated data, which is hard to come by in sufficient quantities. Furthermore, images acquired from one part of the brain are not always representative of another due to the variability in the acquisition and staining processes. Therefore, a classifier trained on the first may perform poorly on the second and additional annotations may be required. To remove this cumbersome requirement, we introduce an Unsupervised Domain Adaptation approach that can leverage annotated data from one brain area to train a classifier that applies to another for which no labeled data is available. To this end, we establish noisy visual correspondences between the two areas and develop a Multiple Instance Learning approach to exploiting them. We demonstrate the benefits of our approach over several baselines for the purpose of synapse and mitochondria segmentation in EM stacks of different parts of mouse brains

Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumes

Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting superior performance, or even human-level accuracy, compared to previous approaches on public mitochondria segmentation datasets. Unfortunately, many of these publications do not make neither the code nor the full training details public to support the results obtained, leading to reproducibility issues and dubious model comparisons. For that reason, and following a recent code of best practices for reporting experimental results, we present an extensive study of the state-of-the-art deep learning architectures for the segmentation of mitochondria on EM volumes, and evaluate the impact in performance of different variations of 2D and 3D U-Net-like models for this task. To better understand the contribution of each component, a common set of pre- and post-processing operations has been implemented and tested with each approach. Moreover, an exhaustive sweep of hyperparameters values for all architectures have been performed and each configuration has been run multiple times to report the mean and standard deviation values of the evaluation metrics. Using this methodology, we found very stable architectures and hyperparameter configurations that consistently obtain state-of-the-art results in the well-known EPFL Hippocampus mitochondria segmentation dataset. Furthermore, we have benchmarked our proposed models on two other available datasets, Lucchi++ and Kasthuri++, where they outperform all previous works. The code derived from this research and its documentation are publicly available

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

Multiscale Centerline Extraction Based on Regression and Projection onto the Set of Elongated Structures

Automatically extracting linear structures from images is a fundamental low-level vision problem with numerous applications in different domains. Centerline detection and radial estimation are the first crucial steps in most Computer Vision pipelines aiming to reconstruct linear structures. Existing techniques rely either on hand-crafted filters, designed to respond to ideal profiles of the linear structure, or on classification-based approaches, which automatically learn to detect centerline points from data. Hand-crafted methods are the most accurate when the content of the image fulfills the ideal model they rely on. However, they lose accuracy in the presence of noise or when the linear structures are irregular and deviate from the ideal case. Machine learning techniques can alleviate this problem. However, they are mainly based on a classification framework. In this thesis, we show that classification is not the best formalism to solve the centerline detection problem. In fact, since the appearance of a centerline point is very similar to the points immediately next to it, the output of a classifier trained to detect centerlines presents low localization accuracy and double responses on the body of the linear structure. To solve this problem, we propose a regression-based formulation for centerline detection. We rely on the distance transform of the centerlines to automatically learn a function whose local maxima correspond to centerline points. The output of our method can be used to directly estimate the location of the centerline, by a simple Non-Maximum Suppression operation, or it can be used as input to a tracing pipeline to reconstruct the graph of the linear structure. In both cases, our method gives more accurate results than state-of-the-art techniques on challenging 2D and 3D datasets. Our method relies on features extracted by means of convolutional filters. In order to process large amount of data efficiently, we introduce a general filter bank approximation scheme. In particular, we show that a generic filter bank can be approximated by a linear combination of a smaller set of separable filters. Thanks to this method, we can greatly reduce the computation time of the convolutions, without loss of accuracy. Our approach is general, and we demonstrate its effectiveness by applying it to different Computer Vision problems, such as linear structure detection and image classification with Convolutional Neural Networks. We further improve our regression-based method for centerline detection by taking advantage of contextual image information. We adopt a multiscale iterative regression approach to efficiently include a large image context in our algorithm. Compared to previous approaches, we use context both in the spatial domain and in the radial one. In this way, our method is also able to return an accurate estimation of the radii of the linear structures. The idea of using regression can also be beneficial for solving other related Computer Vision problems. For example, we show an improvement compared to previous works when applying it to boundary and membrane detection. Finally, we focus on the particular geometric properties of the linear structures. We observe that most methods for detecting them treat each pixel independently and do not model the strong relation that exists between neighboring pixels. As a consequence, their output is geometrically inconsistent. In this thesis, we address this problem by considering the projection of the score map returned by our regressor onto the set of all geometrically admissible ground truth images. We propose an efficient patch-wise approximation scheme to compute the projection. Moreover, we provide conditions under which the projection is exact. We demonstrate the advantage of our method by applying it to four different problems

Hidden Markov Models for Analysis of Multimodal Biomedical Images

Modern advances in imaging technology have enabled the collection of huge amounts of multimodal imagery of complex biological systems. The extraction of information from this data and subsequent analysis are essential in understanding the architecture and dynamics of these systems. Due to the sheer volume of the data, manual annotation and analysis is usually infeasible, and robust automated techniques are the need of the hour. In this dissertation, we present three hidden Markov model (HMM)-based methods for automated analysis of multimodal biomedical images. First, we outline a novel approach to simultaneously classify and segment multiple cells of different classes in multi-biomarker images. A 2D HMM is set up on the superpixel lattice obtained from the input image. Parameters ensuring spatial consistency of labels and high confidence in local class selection are embedded in the HMM framework, and learnt with the objective of maximizing discrimination between classes. Optimal labels are inferred using the HMM, and are aggregated to obtain global multiple object segmentation. We then address the problem of automated spatial alignment of images from different modalities. We propose a probabilistic framework, constructed using a 2D HMM, for deformable registration of multimodal images. The HMM is tailored to capture deformation via state transitions, and modality-specific representation via class-conditional emission probabilities. The latter aspect is premised on the realization that different modalities may provide very different representation for a given class of objects. Parameters of the HMM are learned from data, and hence the method is applicable to a wide array of datasets. In the final part of the dissertation, we describe a method for automated segmentation and subsequent tracking of cells in a challenging target image modality, wherein useful information from a complementary (source) modality is effectively utilized to assist segmentation. Labels are estimated in the source domain, and then transferred to generate preliminary segmentations in the target domain. A 1D HMM-based algorithm is used to refine segmentation boundaries in the target image, and subsequently track cells through a 3D image stack. This dissertation details techniques for classification, segmentation and registration, that together form a comprehensive system for automated analysis of multimodal biomedical datasets

Англійська мова для біологів

Навчальний посібник укладений відповідно до вимог програми рівневого вивчення іноземної мови в університеті і призначений для студентів 1-3 курсів біологічного факультету, які вивчають мову у групах середнього та вищого рівнів. Мета посібника - поглиблення теоретичних і практичних знань студентів з англійської мови, формування та розвиток умінь і навичок сприймати і відтворювати іншомовний науковий фаховий дискурс, розширення словникового запасу загально-наукової та професійної лексики. Посібник містить сучасний автентичний текстовий матеріал, який охоплює базову лексику основних галузей біологічної науки. Практичні завдання укладено з урахуванням новітніх методичних стратегій викладання іноземної мови професійного спрямування. Посібник може бути корисним для магістрантів, аспірантів та науковців-біологів, які самостійно удосконалюють свої знання з англійської мови.CONTENTS : Передмова 5; UNIT 1 7; Lesson 1. Biology – the Science of Life 7; Lesson 2. Life 15; Lesson 3. The Origin of Life 23; Unit 1 Focus Words and Phrases 31; Revision and Additional Practice 1 32; UNIT 2 42; Lesson 1. Macromolecules 42; Lesson 2. Cell as a Basic Unit of Life 53; Lesson 3. Cell Structure 64; Lesson 4. Cell Division 74; Unit 2 Focus Words and Phrases 84; Revision and Additional Practice 2 85; UNIT 3 96; Lesson 1. The Protista 96; Lesson 2. The Bacteria 109; Lesson 3. Viruses 119; Unit 3 Focus Words and Phrases 131; Revision and Additional Practice 3 132; UNIT 4 143 ; Lesson 1. Vascular Plants 143 ; Lesson 2. Development of Gametophytes in Angiosperms 153 ; Lesson 3. Animals 165; Lesson 4. Phylum Chordata 175; Unit 4 Focus Words and Phrases 186; Revision and Additional Practice 4 188; UNIT 5 199; Lesson 1. Anthropogenesis 199; Lesson 2. Evolution 212; Lesson 3. Genetics 225; Lesson 4. Ecology 236; Unit 5 Focus Words and Phrases 248; Revision and Additional Practice 5 250; List of Biology Terms and Biology Related Words 261; References 277; List of Sources 278; Appendix 279; Tapescripts 280

Detección de mitocondrias en células mediante Deep Learning

Este proyecto se basa en la realización de la segmentación de mitocondrias en imágenes celulares obtenidas mediante microscopia electrónica. Para ello se hace uso principalmente de las redes neuronales, y concretamente una arquitectura de red convolucional llamada U-Net. Se presenta un estudio pormenorizado para la búsqueda de la mejor combinación de parámetros y técnicas que permitan obtener una buena segmentación. En el estudio, se han probado diferentes conjuntos de datos públicos utilizados por la comunidad, donde finalmente se han alcanzado resultados comparables al estado del arte