Organizer: de.NBI

Start: Wednesday, 08 September 2021 @ 14:00

End: Wednesday, 08 September 2021 @ 17:00

Description:

Educators:
Janina Mothes, Temesgen H. Dadi (CIBI)

Date:
September 8th, 2021

Location:
GCB 2021 - Online

Contents:
Image analysis is one of the hallmarks of biomedical research due to its wide range of potential applications. This includes enhancing our understanding of brain function by analyzing the connectivity of individual neuronal processes and synapses through serial transmission electron microscopy (EM). Machine learning approaches, in particular convolutional neural networks, allow the automatic segmentation of neural structures in EM images, an important step towards automating the extraction of neuronal connectivity.
The open source KNIME Analytics Platform offers an accessible tool based on the visual programming paradigm to analyse diverse kinds of data, including images. In addition, one can choose from a wide array of data transformations, machine learning algorithms, and visualizations and combine those in one reproducible workflow. KNIME Analytics Platform is freely available from ​https://www.knime.com/downloads​.

In this hands-on tutorial, participants will produce a workflow to create and train a specific Convolutional Network (U-Net) for segmenting cell images. We will start by importing and cleaning up the input data (Transmission Electron Microscopy data). Afterwards, with the help of the KNIME Tensorflow2 integration, we will then train a U-Net model and use the trained network to predict the segmentation of unseen data. In the last step, we visualize our results.

Learning goals:
Participants will learn how to
- Use the open source KNIME Analytics Platform for importing, blending and transforming data
- Work with images in KNIME Analytics Platform
- Train a U-Net model and apply it to unseen data
- Visualize the results

Prerequisites:
For a hands-on tutorial, participants need to bring their own laptop. All the necessary software and data will be made available for download before the tutorial day.

Students (grad/undergrad), researchers, principal investigators with an interest in machine learning, images, data manipulation are welcome to attend the tutorial. A little background on machine learning and imaging data is a plus. We will provide a short introduction to the KNIME Analytics Platform, cell segmentation, and convolutional neural networks, before starting the hands-on sessions.

Keywords:
Computational Workflow, KNIME, Image analysis

Tools:
KNIME

Event type:
  • Meetings and conferences
Cell segmentation using KNIME Analytics Platform and its Tensorflow2 Integration - GCB 2021 https://tess.elixir-europe.org/events/cell-segmentation-using-knime-analytics-platform-and-its-tensorflow2-integration-gcb-2021 Educators: Janina Mothes, Temesgen H. Dadi (CIBI) Date: September 8th, 2021 Location: GCB 2021 - Online Contents: Image analysis is one of the hallmarks of biomedical research due to its wide range of potential applications. This includes enhancing our understanding of brain function by analyzing the connectivity of individual neuronal processes and synapses through serial transmission electron microscopy (EM). Machine learning approaches, in particular convolutional neural networks, allow the automatic segmentation of neural structures in EM images, an important step towards automating the extraction of neuronal connectivity. The open source KNIME Analytics Platform offers an accessible tool based on the visual programming paradigm to analyse diverse kinds of data, including images. In addition, one can choose from a wide array of data transformations, machine learning algorithms, and visualizations and combine those in one reproducible workflow. KNIME Analytics Platform is freely available from ​https://www.knime.com/downloads​. In this hands-on tutorial, participants will produce a workflow to create and train a specific Convolutional Network (U-Net) for segmenting cell images. We will start by importing and cleaning up the input data (Transmission Electron Microscopy data). Afterwards, with the help of the KNIME Tensorflow2 integration, we will then train a U-Net model and use the trained network to predict the segmentation of unseen data. In the last step, we visualize our results. Learning goals: Participants will learn how to - Use the open source KNIME Analytics Platform for importing, blending and transforming data - Work with images in KNIME Analytics Platform - Train a U-Net model and apply it to unseen data - Visualize the results Prerequisites: For a hands-on tutorial, participants need to bring their own laptop. All the necessary software and data will be made available for download before the tutorial day. Students (grad/undergrad), researchers, principal investigators with an interest in machine learning, images, data manipulation are welcome to attend the tutorial. A little background on machine learning and imaging data is a plus. We will provide a short introduction to the KNIME Analytics Platform, cell segmentation, and convolutional neural networks, before starting the hands-on sessions. Keywords: Computational Workflow, KNIME, Image analysis Tools: KNIME 2021-09-08 14:00:00 UTC 2021-09-08 17:00:00 UTC de.NBI [] [] [] meetings_and_conferences [] []