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U-Net Encoder Decoder Architecture for Cell Segmentation

Cell segmentation Cell image Neural network Deep learning Keras
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This workflow creates and trains a Unet for segmenting cell images. The trained network is used to predict the segmentation of unseen data. Data: The training data is a set of 30 sections from a serial section Transmission Electron Microscopy (ssTEM) data set of the Drosophila first instar larva ventral nerve cord (VNC). The microcube measures 2 x 2 x 1.5 microns approx., with a resolution of 4x4x50 nm/pixel. The corresponding binary labels are provided in an in-out fashion, i.e. white for the pixels of segmented objects and black for the rest of pixels (which correspond mostly to membranes). (Source: http://brainiac2.mit.edu/isbi_challenge/home) The required extensions: - KNIME Deep Learning - Keras Integration - KNIME Image Processing - Deep Learning Extension

External resources

  • Link to image data source (ISBI Challenge)

Used extensions & nodes

Created with KNIME Analytics Platform version 4.2.3
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.3

    knime
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    KNIME Deep Learning - Keras Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.1

    knime
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    KNIME Image Processing Trusted extension

    University of Konstanz / KNIME

    Version 1.8.3

    bioml-konstanz
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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.3

    knime
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