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      Modeling and simulation of diffusion and reaction processes during the staining of tissue sections on slides

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          Abstract

          Histological slides are an important tool in the diagnosis of tumors as well as of other diseases that affect cell shapes and distributions. Until now, the research concerning an optimal staining time has been mainly done empirically. In experimental investigations, it is often not possible to stain an already-stained slide with another stain to receive further information. To overcome these challenges, in the present paper a continuum-based model was developed for conducting a virtual (re-)staining of a scanned histological slide. This model is capable of simulating the staining of cell nuclei with the dye hematoxylin (C.I. 75,290). The transport and binding of the dye are modeled (i) along with the resulting RGB intensities (ii). For (i), a coupled diffusion–reaction equation is used and for (ii) Beer–Lambert’s law. For the spatial discretization an approach based on the finite element method (FEM) is used and for the time discretization a finite difference method (FDM). For the validation of the proposed model, frozen sections from human liver biopsies stained with hemalum were used. The staining times were varied so that the development of the staining intensity could be observed over time. The results show that the model is capable of predicting the staining process. The model can therefore be used to perform a virtual (re-)staining of a histological sample. This allows a change of the staining parameters without the need of acquiring an additional sample. The virtual standardization of the staining is the first step towards universal cross-site comparability of histological slides.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00418-022-02118-9.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.

            State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.
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              Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

              Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer.
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                Author and article information

                Contributors
                matthias.meinhardt@uniklinikum-dresden.de
                adrian.ehrenhofer@tu-dresden.de
                Journal
                Histochem Cell Biol
                Histochem Cell Biol
                Histochemistry and Cell Biology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0948-6143
                1432-119X
                6 June 2022
                6 June 2022
                2022
                : 158
                : 2
                : 137-148
                Affiliations
                [1 ]GRID grid.4488.0, ISNI 0000 0001 2111 7257, Technische Universität Dresden, Institute of Solid Mechanics, ; George-Bähr-Straße 3c, 01069 Dresden, Germany
                [2 ]GRID grid.412282.f, ISNI 0000 0001 1091 2917, University Hospital Carl Gustav Carus Dresden, Institute of Pathology, ; Fetscherstraße 74, 01307 Dresden, Germany
                [3 ]GRID grid.4488.0, ISNI 0000 0001 2111 7257, Technische Universität Dresden, Dresden Center for Intelligent Materials, School of Engineering Sciences, ; George-Bähr-Straße 3c, 01069 Dresden, Germany
                Author information
                http://orcid.org/0000-0002-2370-8381
                Article
                2118
                10.1007/s00418-022-02118-9
                9338144
                35666313
                aa2a9ce3-dda3-4ae4-87bb-e9b2fab6ee91
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 May 2022
                Funding
                Funded by: Free State of Saxony and TU Dresden
                Award ID: Dresden Center for Intelligent Materials
                Award Recipient :
                Funded by: Technische Universität Dresden (1019)
                Categories
                Original Paper
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2022

                Cell biology
                histological staining,finite element simulation,numerical simulation,reaction–diffusion equation,image segmentation,beer–lambert law

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