Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p

Image thresholding techniques for localization of sub-resolution fluorescent biomarkers

In this article, we explore adaptive global and local segmentation techniques for a lab-on-chip nutrition monitoring system (NutriChip). The experimental setup consists of Caco-2 intestinal cells that can be artificially stimulated to trigger an immune response. The eventual response is optically monitored using immunofluoresence techniques targeting toll-like receptor 2 (TLR2). Two problems of interest need to be addressed by means of image processing. First, a new cell sample must be properly classified as stimulated or not. Second, the location of the stained TLR2 must be recovered in case the sample has been stimulated. The algorithmic approach to solving these problems is based on the ability of a segmentation technique to properly segment fluorescent spots. The sample classification is based on the amount and intensity of the segmented pixels, while the various segmenting blobs provide an approximate localization of TLR2. A novel local thresholding algorithm and three well-known spot segmentation techniques are compared in this study. Quantitative assessment of these techniques based on real and synthesized data demonstrates the improved segmentation capabilities of the proposed algorithm

Multiscale image analysis of calcium dynamics in cardiac myocytes

Cardiac myocytes constitute a unique physiological system. They are the muscle cells that build up heart tissue and provide the force to pump blood by synchronously contracting at every beat. This contraction is regulated by calcium concentration, among other ions, which exhibits a very complex behaviour, rich in dynamical states at the molecular, cellular and tissue levels. Details of such dynamical patterns are closely related to the mechanisms responsible for cardiac function and also cardiac disease, which is the first cause of death in the modern world. The emerging field of translational cardiology focuses on the study of how such mechanisms connect and influence each other across spatial and temporal scales finally yielding to a certain clinical condition. In order to study such patterns, we benefit from the recent and very important advances in the field of experimental cell physiology. In particular, fluorescence microscopy allows us to observe the distribution of calcium in the cell with a spatial resolution below the micron and a frame rate around the millisecond, thus providing a very accurate monitoring of calcium fluxes in the cell. This thesis is the result of over five years' work on biological signal and digital image processing of cardiac cells. During this period of time the aim has been to develop computational techniques for extracting quantitative data of physiological relevance from microscopy images at different scales. The two main subjects covered in the thesis are image segmentation and classification methods applied to fluorescence microscopy imaging of cardiac myocytes. These methods are applied to a variety of problems involving different space and time scales such as the localisation of molecular receptors, the detection and characterisation of spontaneous calcium-release events and the propagation of calcium waves across a culture of cardiac cells. The experimental images and data have been provided by four internationally renowned collaborators in the field. It is thanks to them and their teams that this thesis has been possible. They are Dr. Leif Hove-Madsen from the Institut de Ciències Cardiovasculars de Catalunya in Barcelona, Prof. S. R. Wayne Chen from the Department of Physiology and Pharmacology in the Libin Cardiovascular Institute of Alberta, University of Calgary, Dr. Peter P. Jones from the Department of Physiology in the University of Otago, and Prof. Glen Tibbits from the Department of Biomedical Physiology & Kinesiology at the Simon Fraser University in Vancouver. The work belongs to the biomedical engineering discipline, focusing on the engineering perspective by applying physics and mathematics to solve biomedical problems. Specifically, we frame our contributions in the field of computational translational cardiology, attempting to connect molecular mechanisms in cardiac cells up to cardiac disease by developing signal and image-processing methods and machine-learning methods that are scalable through the different scales. This computational approach allows for a quantitative, robust and reproducible analysis of the experimental data and allows us to obtain results that otherwise would not be possible by means of traditional manual methods. The results of the thesis provide specific insight into different cell mechanisms that have a non-negligible impact at the clinical level. In particular, we gain a deeper knowledge of cell mechanisms related to cardiac arrhythmia, fibrillation phenomena, the emergence of alternans and anomalies in calcium handling due to cell ageing.Els cardiomiòcits constitueixen un sistema fisiològic únic. Són les cèl·lules muscular que formen el cor i proporcionen la força per bombar la sang fent una contracció a cada batec. La regulació d'aquesta contracció es fa mitjançant concentració de calci (entre d'altres ions) i presenta una dinàmica molt complexa tant a l'escala molecular, cel·lular i de teixit. Detalls d'aquesta dinàmica estan fortament relacionats amb la funció cardíaca i per sobre de tot amb patologies cardíaques. La disciplina emergent de la cardiologia translacional es centra en l'estudi de com aquests mecanismes es connecten i s'influencien entre sí a través de diferents escales temporals i espacials finalment donant lloc a condicions clíniques. Per estudiar aquests patrons ens beneficiem dels recents avenços en fisiologia i biologia cel·lular. En particular, la microscòpia de fluorescència ens permet observar la distribució de calci dins una cèl·lula amb una resolució espacial per sota de la micra i temporal per sota del mil·lisegon, permetent un monitoratge acurat dels fluxos de calci en la cèl·lula cardíaca. Aquesta tesi és el resultat de més de cinc anys de feina en processament de senyal i imatge de cardiomiòcits humans. Durant aquest període de temps l'objectiu principal ha estat desenvolupar tècniques computacionals per extraure dades d'imatges de microscòpia amb rellevància fisiològica. Els dos temes principals coberts a la tesi són segmentació d'imatges i classificadors, aplicats a imatges de microscòpia de fluorescència de cardiomiòcits. Els mètodes s'apliquen a diferents problemes involucrant diverses escales espacials i temporals, des de determinar la posició de receptors a l’escala molecular passant detectar i caracteritzar alliberament espontani de calci intracel·lular fins a la propagació d'ones de calci en un cultiu de cèl·lules cardíaques. Les dades experimentals han estat proporcionades per quatre col·laboradors de renom internacional. És gràcies a ells i els seus equips que aquesta tesi ha estat possible. Són el Dr. Leif Hove-Madsen de l'Institut de Ciències Cardiovasculars de Catalunya a Barcelona, el Dr. S.R. Wayne Chen del Department of Physiology and Pharmacology al Libin Cardiovascular Institute of Alberta, University of Calgary, el Dr. Peter P. Jones del Department of Physiology a la University of Otago, i el Dr. Glen Tibbits del Department of Biomedical Physiology & Kinesiology de la Simon Fraser University a Vancouver. El treball pertany a la disciplina de la enginyeria biomèdica, fent èmfasi a la perspectiva de l'enginyeria, aplicant física i matemàtiques per solucionar problemes de la biomedicina. Específicament, s'emmarca en la cardiologia translacional computacional, mirant de connectar mecanismes a l’escala molecular amb patologies cardíaques mitjançant tècniques de processament de dades i aprenentatge automàtic que són escalables a les diferents escales d’aplicació. Aquest enfocament computacional permet una anàlisi quantitatiu, robust i reproduïble de les dades experimentals i ens permet d'obtenir resultats que serien impossibles d'assolir mitjançant els tradicionals mètodes manuals. Els resultats que proporciona la tesi han permès aprofundir en l'enteniment de diferents mecanismes fisiològics amb impacte en l'àmbit clínic. Particularment hem permès d’assolir coneixements relacionats amb l'arítmia cardíaca, la fibril·lació, processos d'alternança i anomalies relacionades amb l’envelliment

Image Analysis Algorithms for Single-Cell Study in Systems Biology

With the contiguous shift of biology from a qualitative toward a quantitative field of research, digital microscopy and image-based measurements are drawing increased interest. Several methods have been developed for acquiring images of cells and intracellular organelles. Traditionally, acquired images are analyzed manually through visual inspection. The increasing volume of data is challenging the scope of manual analysis, and there is a need to develop methods for automated analysis. This thesis examines the development and application of computational methods for acquisition and analysis of images from single-cell assays. The thesis proceeds with three different aspects.First, a study evaluates several methods for focusing microscopes and proposes a novel strategy to perform focusing in time-lapse imaging. The method relies on the nature of the focus-drift and its predictability. The study shows that focus-drift is a dynamical system with a small randomness. Therefore, a prediction-based method is employed to track the focus-drift overtime. A prototype implementation of the proposed method is created by extending the Nikon EZ-C1 Version 3.30 (Tokyo, Japan) imaging platform for acquiring images with a Nikon Eclipse (TE2000-U, Nikon, Japan) microscope.Second, a novel method is formulated to segment individual cells from a dense cluster. The method incorporates multi-resolution analysis with maximum-likelihood estimation (MAMLE) for cell detection. The MAMLE performs cell segmentation in two phases. The initial phase relies on a cutting-edge filter, edge detection in multi-resolution with a morphological operator, and threshold decomposition for adaptive thresholding. It estimates morphological features from the initial results. In the next phase, the final segmentation is constructed by boosting the initial results with the estimated parameters. The MAMLE method is evaluated with de novo data sets as well as with benchmark data from public databases. An empirical evaluation of the MAMLE method confirms its accuracy.Third, a comparative study is carried out on performance evaluation of state-ofthe-art methods for the detection of subcellular organelles. This study includes eleven algorithms developed in different fields for segmentation. The evaluation procedure encompasses a broad set of samples, ranging from benchmark data to synthetic images. The result from this study suggests that there is no particular method which performs superior to others in the test samples. Next, the effect of tetracycline on transcription dynamics of tetA promoter in Escherichia coli (E. coli ) cells is studied. This study measures expressions of RNA by tagging the MS2d-GFP vector with a target gene. The RNAs are observed as intracellular spots in confocal images. The kernel density estimation (KDE) method for detecting the intracellular spots is employed to quantify the individual RNA molecules.The thesis summarizes the results from five publications. Most of the publications are associated with different methods for imaging and analysis of microscopy. Confocal images with E. coli cells are targeted as the primary area of application. However, potential applications beyond the primary target are also made evident. The findings of the research are confirmed empirically

Particle Filtering Methods for Subcellular Motion Analysis

Advances in fluorescent probing and microscopic imaging technology have revolutionized biology in the past decade and have opened the door for studying subcellular dynamical processes. However, accurate and reproducible methods for processing and analyzing the images acquired for such studies are still lacking. Since manual image analysis is time consuming, potentially inaccurate, and poorly reproducible, many biologically highly relevant questions are either left unaddressed, or are answered with great uncertainty. The subject of this thesis is particle filtering methods and their application for multiple object tracking in different biological imaging applications. Particle filtering is a technique for implementing recursive Bayesian filtering by Monte Carlo sampling. A fundamental concept behind the Bayesian approach for performing inference is the possibility to encode the information about the imaging system, possible noise sources, and the system dynamics in terms of probability density functions. In this thesis, a set of novel PF based metho

Exploration of cyber-physical systems for GPGPU computer vision-based detection of biological viruses

This work presents a method for a computer vision-based detection of biological viruses in PAMONO sensor images and, related to this, methods to explore cyber-physical systems such as those consisting of the PAMONO sensor, the detection software, and processing hardware. The focus is especially on an exploration of Graphics Processing Units (GPU) hardware for “General-Purpose computing on Graphics Processing Units” (GPGPU) software and the targeted systems are high performance servers, desktop systems, mobile systems, and hand-held systems. The first problem that is addressed and solved in this work is to automatically detect biological viruses in PAMONO sensor images. PAMONO is short for “Plasmon Assisted Microscopy Of Nano-sized Objects”. The images from the PAMONO sensor are very challenging to process. The signal magnitude and spatial extension from attaching viruses is small, and it is not visible to the human eye on raw sensor images. Compared to the signal, the noise magnitude in the images is large, resulting in a small Signal-to-Noise Ratio (SNR). With the VirusDetectionCL method for a computer vision-based detection of viruses, presented in this work, an automatic detection and counting of individual viruses in PAMONO sensor images has been made possible. A data set of 4000 images can be evaluated in less than three minutes, whereas a manual evaluation by an expert can take up to two days. As the most important result, sensor signals with a median SNR of two can be handled. This enables the detection of particles down to 100 nm. The VirusDetectionCL method has been realized as a GPGPU software. The PAMONO sensor, the detection software, and the processing hardware form a so called cyber-physical system. For different PAMONO scenarios, e.g., using the PAMONO sensor in laboratories, hospitals, airports, and in mobile scenarios, one or more cyber-physical systems need to be explored. Depending on the particular use case, the demands toward the cyber-physical system differ. This leads to the second problem for which a solution is presented in this work: how can existing software with several degrees of freedom be automatically mapped to a selection of hardware architectures with several hardware configurations to fulfill the demands to the system? Answering this question is a difficult task. Especially, when several possibly conflicting objectives, e.g., quality of the results, energy consumption, and execution time have to be optimized. An extensive exploration of different software and hardware configurations is expensive and time-consuming. Sometimes it is not even possible, e.g., if the desired architecture is not yet available on the market or the design space is too big to be explored manually in reasonable time. A Pareto optimal selection of software parameters, hardware architectures, and hardware configurations has to be found. To achieve this, three parameter and design space exploration methods have been developed. These are named SOG-PSE, SOG-DSE, and MOGEA-DSE. MOGEA-DSE is the most advanced method of these three. It enables a multi-objective, energy-aware, measurement-based or simulation-based exploration of cyber-physical systems. This can be done in a hardware/software codesign manner. In addition, offloading of tasks to a server and approximate computing can be taken into account. With the simulation-based exploration, systems that do not exist can be explored. This is useful if a system should be equipped, e.g., with the next generation of GPUs. Such an exploration can reveal bottlenecks of the existing software before new GPUs are bought. With MOGEA-DSE the overall goal—to develop a method to automatically explore suitable cyber-physical systems for different PAMONO scenarios—could be achieved. As a result, a rapid, reliable detection and counting of viruses in PAMONO sensor data using high-performance, desktop, laptop, down to hand-held systems has been made possible. The fact that this could be achieved even for a small, hand-held device is the most important result of MOGEA-DSE. With the automatic parameter and design space exploration 84% energy could be saved on the hand-held device compared to a baseline measurement. At the same time, a speedup of four and an F-1 quality score of 0.995 could be obtained. The speedup enables live processing of the sensor data on the embedded system with a very high detection quality. With this result, viruses can be detected and counted on a mobile, hand-held device in less than three minutes and with real-time visualization of results. This opens up completely new possibilities for biological virus detection that were not possible before