Single-particle analysis by electron microscopy is a well established technique for analyzing the three-dimensional structures of biological macromolecules. Besides its ability to produce high-resolution structures, it also provides insights into the dynamic behavior of the structures by elucidating their conformational variability. Here, the different image-processing methods currently available to study continuous conformational changes are reviewed.

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Three dimensional electron microscopy is becoming a very data-intensive field in which vast amounts of experimental images are acquired at high speed. To manage such large-scale projects, we had previously developed a modular workflow system called Scipion (de la Rosa-Trevín et al., 2016). We present here a major extension of Scipion that allows processing of EM images while the data is being acquired. This approach helps to detect problems at early stages, saves computing time and provides users with a detailed evaluation of the data quality before the acquisition is finished. At present, Scipion has been deployed and is in production mode in seven Cryo-EM facilities throughout the world.

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Electron cryomicroscopy (cryo-EM) is essential for the study and functional understanding of non-crystalline macromolecules such as proteins. These molecules cannot be imaged using X-ray crystallography or other popular methods. Cryo-EM has been successfully used to visualize molecules such as ribosomes, viruses, and ion channels, for example. Obtaining structural models of these at various

One of the big challenges in the recognition of biomedical samples is the lack of large annotated datasets. Their relatively small size, when compared to datasets like ImageNet, typically leads to problems with ecient training of current machine learning algorithms. However, the recent development of generative adversarial networks (GANs) appears to be a step towards addressing this issue. In this study, we focus on one instance of GANs, which is known as deep convolutional generative adversarial network (DCGAN). It gained a lot of attention recently because of its stability in generating realistic articial images. Our article explores the possibilities of using DCGANs for generating HEp-2 images. We trained multiple DCGANs and generated several datasets of HEp-2 images. Subsequently, we combined them with traditional augmentation and evaluated over three dierent deep learning congurations. Our article demonstrates high visual quality of generated images, which is also supported by state-of-the-art classication results. Keywords: Deep learning • Image recognition • HEp-2 image classication • GAN • CNN • GoogLeNet • VGG-16 • Inception-v3 • Transfer learning.

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The image descriptors are a very useful tool in the task of classification. In biomedical image analysis, they may characterize either the shape or the internal structure of studied objects. Both characteristics are very important. When analysing cells, their shape is usually determined first. In the second step, their mask may be used for the selection of the area where the texture descriptor should be applied.In this paper, we are going to focus on the texture-based image descriptors called Tamura features. For their basic properties, they seem to be a very promising tool applicable to the biomedical image data. We will apply them to selected types of cell lines and test how they perform. We will also introduce their extension to higher dimensions and show that they give even better results than in the 2D case.