Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization

Big data in cryoEM: automated collection, processing and accessibility of EM data

The scope and complexity of cryogenic electron microscopy (cryoEM) data has greatly increased, and will continue to do so, due to recent and ongoing technical breakthroughs that have led to much improved resolutions for macromolecular structures solved using this method. This big data explosion includes single particle data as well as tomographic tilt series, both generally acquired as direct detector movies of ∼10-100 frames per image or per tilt-series. We provide a brief survey of the developments leading to the current status, and describe existing cryoEM pipelines, with an emphasis on the scope of data acquisition, methods for automation, and use of cloud storage and computing.

Automatic segmentation of myocardium from black-blood MR images using entropy and local neighborhood information

By using entropy and local neighborhood information, we present in this study a robust adaptive Gaussian regularizing Chan-Vese (CV) model to segment the myocardium from magnetic resonance images with intensity inhomogeneity. By utilizing the circular Hough transformation (CHT) our model is able to detect epicardial and endocardial contours of the left ventricle (LV) as circles automatically, and the circles are used as the initialization. In the cost functional of our model, the interior and exterior energies are weighted by the entropy to improve the robustness of the evolving curve. Local neighborhood information is used to evolve the level set function to reduce the impact of the heterogeneity inside the regions and to improve the segmentation accuracy. An adaptive window is utilized to reduce the sensitivity to initialization. The Gaussian kernel is used to regularize the level set function, which can not only ensure the smoothness and stability of the level set function, but also eliminate the traditional Euclidean length term and re-initialization. Extensive validation of the proposed method on patient data demonstrates its superior performance over other state-of-the-art methods.

Particle quality assessment and sorting for automatic and semiautomatic particle-picking techniques

Three-dimensional reconstruction of biological specimens using electron microscopy by single particle methodologies requires the identification and extraction of the imaged particles from the acquired micrographs. Automatic and semiautomatic particle selection approaches can localize these particles, minimizing the user interaction, but at the cost of selecting a non-negligible number of incorrect particles, which can corrupt the final three-dimensional reconstruction. In this work, we present a novel particle quality assessment and sorting method that can separate most erroneously picked particles from correct ones. The proposed method is based on multivariate statistical analysis of a particle set that has been picked previously using any automatic or manual approach. The new method uses different sets of particle descriptors, which are morphology-based, histogram-based and signal to noise analysis based. We have tested our proposed algorithm with experimental data obtaining very satisfactory results. The algorithm is freely available as a part of the Xmipp 3.0 package [http://xmipp.cnb.csic.es].

A robust medical image segmentation method using KL distance and local neighborhood information

In this paper, we propose an improved Chan-Vese (CV) model that uses Kullback-Leibler (KL) distances and local neighborhood information (LNI). Due to the effects of heterogeneity and complex constructions, the performance of level set segmentation is subject to confounding by the presence of nearby structures of similar intensity, preventing it from discerning the exact boundary of the object. Moreover, the CV model cannot usually obtain accurate results in medical image segmentation in cases of optimal configuration of controlling parameters, which requires substantial manual intervention. To overcome the above deficiency, we improve the segmentation accuracy by the usage of KL distance and LNI, thereby introducing the image local characteristics. Performance evaluation of the present method was achieved through experiments on the synthetic images and a series of real medical images. The extensive experimental results showed the superior performance of the proposed method over the state-of-the-art methods, in terms of both robustness and efficiency.

Reference-free particle selection enhanced with semi-supervised machine learning for cryo-electron microscopy

Reference-based methods have dominated the approaches to the particle selection problem, proving fast, and accurate on even the most challenging micrographs. A reference volume, however, is not always available and compiling a set of reference projections from the micrographs themselves requires significant effort to attain the same level of accuracy. We propose a reference-free method to quickly extract particles from the micrograph. The method is augmented with a new semi-supervised machine-learning algorithm to accurately discriminate particles from contaminants and noise.

A clarification of the terms used in comparing semi-automated particle selection algorithms in cryo-EM

Many cyro-EM datasets are heterogeneous stemming from molecules undergoing conformational changes. The need to characterize each of the substrates with sufficient resolution entails a large increase in the data flow and motivates the development of more effective automated particle selection algorithms. Concepts and procedures from the machine-learning field are increasingly employed toward this end. However, a review of recent literature has revealed a discrepancy in terminology of the performance scores used to compare particle selection algorithms, and this has subsequently led to ambiguities in the meaning of claimed performance. In an attempt to curtail the perpetuation of this confusion and to disentangle past mistakes, we review the performance of published particle selection efforts with a set of explicitly defined performance scores using the terminology established and accepted within the field of machine learning.

Automation in single-particle electron microscopy connecting the pieces

Throughout the history of single-particle electron microscopy (EM), automated technologies have seen varying degrees of emphasis and development, usually depending upon the contemporary demands of the field. We are currently faced with increasingly sophisticated devices for specimen preparation, vast increases in the size of collected data sets, comprehensive algorithms for image processing, sophisticated tools for quality assessment, and an influx of interested scientists from outside the field who might lack the skills of experienced microscopists. This situation places automated techniques in high demand. In this chapter, we provide a generic definition of and discuss some of the most important advances in automated approaches to specimen preparation, grid handling, robotic screening, microscope calibrations, data acquisition, image processing, and computational infrastructure. Each section describes the general problem and then provides examples of how that problem has been addressed through automation, highlighting available processing packages, and sometimes describing the particular approach at the National Resource for Automated Molecular Microscopy (NRAMM). We contrast the more familiar manual procedures with automated approaches, emphasizing breakthroughs as well as current limitations. Finally, we speculate on future directions and improvements in automated technologies. Our overall goal is to present automation as more than simply a tool to save time. Rather, we aim to illustrate that automation is a comprehensive and versatile strategy that can deliver biological information on an unprecedented scale beyond the scope available with classical manual approaches.

Automatic particle selection from electron micrographs using machine learning techniques

The 3D reconstruction of biological specimens using Electron Microscopy is currently capable of achieving subnanometer resolution. Unfortunately, this goal requires gathering tens of thousands of projection images that are frequently selected manually from micrographs. In this paper we introduce a new automatic particle selection that learns from the user which particles are of interest. The training phase is semi-supervised so that the user can correct the algorithm during picking and specifically identify incorrectly picked particles. By treating such errors specially, the algorithm attempts to minimize the number of false positives. We show that our algorithm is able to produce datasets with fewer wrongly selected particles than previously reported methods. Another advantage is that we avoid the need for an initial reference volume from which to generate picking projections by instead learning which particles to pick from the user. This package has been made publicly available in the open-source package Xmipp.

Ab initio maximum likelihood reconstruction from cryo electron microscopy images of an infectious virion of the tailed bacteriophage P22 and maximum likelihood versions of Fourier Shell Correlation appropriate for measuring resolution of spherical or cylindrical objects

A maximum likelihood reconstruction method for an asymmetric reconstruction of the infectious P22 bacteriophage virion is described and demonstrated on a subset of the images used in [Lander, G.C., Tang, L., Casjens, S.R., Gilcrease, E.B., Prevelige, P., Poliakov, A., Potter, C.S., Carragher, B., Johnson, J.E., 2006. The structure of an infectious P22 virion shows the signal for headful DNA packaging. Science 312(5781), 1791-1795]. The method makes no assumptions at any stage regarding the structure of the phage tail or the relative rotational orientation of the phage tail and capsid but rather the structure and the rotation angle are determined as a part of the analysis. A statistical method for determining resolution consistent with maximum likelihood principles based on ideas for cylinders analogous to the ideas for spheres that are embedded in the Fourier Shell Correlation method is described and demonstrated on the P22 reconstruction. With a correlation threshold of .95, the resolution in the tail measured radially is greater than 0.0301A(-1) (33.3A) and measured axially is greater than 0.0142A(-1) (70.6A) both with probability p=0.02.