Cell nuclei detection using globally optimal active contours with shape prior

Cell nuclei detection in fluorescent microscopic images is an important and time consuming task for a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make this a challenging task for automated detection of individual nuclei using image analysis. This paper proposes a novel and robust detection method based on the active contour framework. The method exploits prior knowledge of the nucleus shape in order to better detect individual nuclei. The method is formulated as the optimization of a convex energy function. The proposed method shows accurate detection results even for clusters of nuclei where state of the art methods fail

Computational efficient segmentation of cell nuclei in 2D and 3D fluorescent micrographs

This paper proposes a new segmentation technique developed for the segmentation of cell nuclei in both 2D and 3D fluorescent micrographs. The proposed method can deal with both blurred edges as with touching nuclei. Using a dual scan line algorithm its both memory as computational efficient, making it interesting for the analysis of images coming from high throughput systems or the analysis of 3D microscopic images. Experiments show good results, i.e. recall of over 0.98

Cellular Pattern Quantication and Automatic Bench-marking Data-set Generation on confocal microscopy images

The distribution, directionality and motility of the actin fibers control cell shape, affect cell function and are different in cancer versus normal cells. Quantification of actin structural changes is important for further understanding differences between cell types and for elucidation the effects and dynamics of drug interactions. We propose an image analysis framework to quantify the F-actin organization patterns in response to different pharmaceutical treatments.The main problems addressed include which features to quantify and what quantification measurements to compute when dealing with unlabeled confocal microscopy images. The resultant numerical features are very effective to profile the functional mechanism and facilitate the comparison of different drugs. The analysis software is originally implemented in Matlab and more recently the most time consuming part in the feature extraction stage is implemented onto the NVIDIA GPU using CUDA where we obtain 15 to 20 speedups for different sizes of image. We also propose a computational framework for generating synthetic images for validation purposes. The validation for the feature extraction is done by visual inspection and the validation for quantification is done by comparing them with well-known biological facts. Future studies will further validate the algorithms, and elucidate the molecular pathways and kinetics underlying the F-actin changes. This is the first study quantifying different structural formations of the same protein in intact cells. Since many anti-cancer drugs target the cytoskeleton, we believe that the quantitative image analysis method reported here will have broad applications to understanding the mechanisms of candidate pharmaceutical

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

In cryo-electron microscopy (EM), molecular structures are determined from large numbers of projection images of individual particles. To harness the full power of this single-molecule information, we use the Bayesian inference of EM (BioEM) formalism. By ranking structural models using posterior probabilities calculated for individual images, BioEM in principle addresses the challenge of working with highly dynamic or heterogeneous systems not easily handled in traditional EM reconstruction. However, the calculation of these posteriors for large numbers of particles and models is computationally demanding. Here we present highly parallelized, GPU-accelerated computer software that performs this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI parallelization combined with both CPU and GPU computing. The resulting BioEM software scales nearly ideally both on pure CPU and on CPU+GPU architectures, thus enabling Bayesian analysis of tens of thousands of images in a reasonable time. The general mathematical framework and robust algorithms are not limited to cryo-electron microscopy but can be generalized for electron tomography and other imaging experiments

Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms

The zip archive contains real images showing macrophages. (ZIP 28979 kb

Segmentation of Fluorescence Microscopy Cell Images Using Unsupervised Mining

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu’s threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu’s threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications

Cross-validation tests for cryo-electron microscopy using an independent set of images

ABSTRACT: In addition to the chemical composition, information about the three-dimensional structure of a biomolecule is vital for understanding its biological function. For many years, resolv- ing structures of biomolecules was exclusive of X-ray crystallography and nuclear magnetic resonance (NMR) techniques. However, due to technological and software improvements, cryo-electron microscopy (cryo-EM) has emerged as an alternative for resolving complexes that were infeasible for crystallization or too large for NMR. Currently, cryo-EM is able to provide near-atomic resolution and close-to-native structures . Moreover, it enables extracting dynamical information, such as free-energy landscapes, from thermal states in the micrographs. The “resolution revolution” in cryo-EM has provoked an avalanche of reported cryo-EM maps. Recent statistics show an exponentially-growing number of reported maps spatially resolved by cryo-EM with their mean resolution decreasing from ∼ 10 Å (in 2013) to 4 Å (for 2018). The resolution revolution brings with it the need of creating robust and reliable methodolo- gies to validate the increasingly large number of maps. Some advances have been done along these lines: the tilt-pair analysis , the gold-standard procedure and the high-frequency randomization have shown to be reliable validation tools. However, it has recently been shown that these methods remain sensitive to overfitting (treating noise as true signal) and subjective criteria. In this work, I will present a novel methodology for validating cryo-EM maps. The method is based on cross-validation criteria where the reconstructed maps are compared against a set of experimental images (raw data) not used in the reconstruction procedure. Such comparison is carried out by calculating the probability that an image is the projection of a given map. The information from these probabilities led us to propose two validation criteria, which are tested over three well-behaved systems and two systems that present overfitting. The results prove that our methodology is able to identify overfitted maps