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

Nanodiamond landmarks for subcellular multimodal optical and electron imaging.

There is a growing need for biolabels that can be used in both optical and electron microscopies, are non-cytotoxic, and do not photobleach. Such biolabels could enable targeted nanoscale imaging of sub-cellular structures, and help to establish correlations between conjugation-delivered biomolecules and function. Here we demonstrate a sub-cellular multi-modal imaging methodology that enables localization of inert particulate probes, consisting of nanodiamonds having fluorescent nitrogen-vacancy centers. These are functionalized to target specific structures, and are observable by both optical and electron microscopies. Nanodiamonds targeted to the nuclear pore complex are rapidly localized in electron-microscopy diffraction mode to enable "zooming-in" to regions of interest for detailed structural investigations. Optical microscopies reveal nanodiamonds for in-vitro tracking or uptake-confirmation. The approach is general, works down to the single nanodiamond level, and can leverage the unique capabilities of nanodiamonds, such as biocompatibility, sensitive magnetometry, and gene and drug delivery

Spectral imaging in preclinical research and clinical pathology.

Spectral imaging methods are attracting increased interest from researchers and practitioners in basic science, pre-clinical and clinical arenas. A combination of better labeling reagents and better optics creates opportunities to detect and measure multiple parameters at the molecular and cellular level. These tools can provide valuable insights into the basic mechanisms of life, and yield diagnostic and prognostic information for clinical applications. There are many multispectral technologies available, each with its own advantages and limitations. This chapter will present an overview of the rationale for spectral imaging, and discuss the hardware, software and sample labeling strategies that can optimize its usefulness in clinical settings

Automated Analysis of Fluorescent Microscopic Images to Identify Protein-Protein Interactions

The identification and confirmation of protein interactions significantly challenges the field of systems biology and related bio-computational efforts. The identification of protein-protein interactions along with their spatial and temporal localization is useful for assigning functional information to proteins. Fluorescence microscopy is an ideal method for assessing protein localization and interactions as a number of techniques and reagents have been described. Historically, data sets obtained from fluorescence microscopy have been analyzed manually, a process that is both time consuming and tedious. The development of an automated system that can measure the location and dynamics of interacting proteins inside a live cell is of high priority. This paper describes an automated image analysis system used to identify an interaction between two proteins of interest. These proteins are fused to either Green Fluorescent Protein (GFP) or DivIVA, a bacterial cell division protein that localizes to the cell poles. Upon induction of the DivIVA fusion protein, the GFP-fusion protein is recruited to the cell poles if a positive interaction occurs. There were many problems that came into the picture during the development for an automated system to identify these positive interactions. There were basic segmentation and edge detection problems and the problems caused by inclusion bodies (will be discussed in the sections to follow). Different known procedures to obtain thresholds, and edges were evaluated and the apt ones for our analysis were implemented. A proper flow of advanced image processing and feature extraction algorithms was laid out. These steps were used to analyze the datasets of acquired images. Various methods applied are discussed in detail. The experiments conducted along with the results generated are discussed extensively. A statistical feature set used to quantify the image based information and to aid in the determination of a positive interaction is developed. Various image processing and feature extraction algorithms used to analyze fluorescence microscopic images were also applied to Atomic force microscopic images with a few modifications. There was a basic problem of uneven background noise and this was removed using a common procedure that is used to remove uneven illumination in DIC images. These AFM images were analyzed and quantized using numerical descriptors defined during the analysis of fluorescent microscopic images

Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application