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Cell Segmentation

Unet Tensorflow2 TF2 Cell segmentation Cell image
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This workflow uses Tensorflow2 to create and train 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) In order to run this example, you need a local Python installation that includes TensorFlow 2. TensorFlow 2 must be selected to be used for the "DL Python" nodes on the "Python Deep Learning" preferences page. Please refer to https://docs.knime.com/latest/deep_learning_installation_guide/#dl_python_setup for installation recommendations and further information.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.2.0
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

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    KNIME Deep Learning - Keras Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

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    KNIME Deep Learning - TensorFlow 2 Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

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    KNIME Image Processing Trusted extension

    University of Konstanz / KNIME

    Version 1.8.3

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