Software tools for automated transmission electron microscopy

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Abstract

High-throughput data collection for electron microscopy (EM) demands appropriate software tools. We present a combination of such tools that enable automated acquisition guided by image analysis for a variety of transmission EM acquisition schemes. Py-EM interfaces with the microscope control software SerialEM and expands its updated flexible automation features by image analysis to enable feedback microscopy. We demonstrate dose-reduction in cryo-EM experiments, fully automated acquisition of every cell in a plastic section, and automated targeting on serial sections for volumetric imaging across multiple grids.

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Most cited references 26

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Python for Scientific Computing

Travis E. Oliphant (2007)

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Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

Nima Tajbakhsh, Jae Y. Shin, Suryakanth R Gurudu … (2016)

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.

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Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes.

Christian Conrad, Catherine Chapuis, Rolf Kabbe … (2010)

Despite our rapidly growing knowledge about the human genome, we do not know all of the genes required for some of the most basic functions of life. To start to fill this gap we developed a high-throughput phenotypic screening platform combining potent gene silencing by RNA interference, time-lapse microscopy and computational image processing. We carried out a genome-wide phenotypic profiling of each of the approximately 21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. As part of the Mitocheck consortium, this study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.