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EMBL BioImage Data Analysis

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RT @IgnacioArganda: #DeepImageJ turn now! 😍 Now even more compatible with more platforms besides @FijiSc Fantastic job by @gomez_mariscal a…
About 1 day, 11 hours ago by: Kota Miura (@cmci_)

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beautiful lab website! https://t.co/X3YaujiVFE
About 1 day, 19 hours ago by: Kota Miura (@cmci_)

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moderately reproducible image analysis (some parts are without scripts.) protocols combining Imaris, Fiji, Matlab,… https://t.co/YDihoLmCZG
About 1 day, 19 hours ago by: Kota Miura (@cmci_)

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wow... https://t.co/sAjaZ0irwN
About 1 week, 1 day ago by: Kota Miura (@cmci_)

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@notjustmoore @haesleinhuepf @LaineBioImaging @alicepyne @RSE_Sheffield @sebastianmunck How about reverse coding -… https://t.co/8MhZOclxyn
About 1 week, 1 day ago by: Kota Miura (@cmci_)

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does this have to be deep-learning? https://t.co/hMrdeCfFO5
About 1 week, 4 days ago by: Kota Miura (@cmci_)
documents:121019htm

2012 EMBO High-throughput Microscopy Practical Course

Dates: 2012.10.15-20 Place: ATC, EMBL Heidelberg

    1. With Andrea Boni @ Ellenberg Lab
    2. Abstract: The students will learn basics of image analysis of time lapse data. We will first go through preprocessing steps: filtering, noise reduction and background subtraction. We then move on to image thresholding and segmentation techniques, which allow us to objectively define region of interest (ROI) where the temporal dynamics of signal intensity are measured. Automation is an esseitial aspect in highthroughput imaging. In this session, students will be guided to learn how to automate the imaging processing steps that we went through manually. Scripting is necessary for automation. For this reason, a brief introduction to code ImageJ macro will be provided and students will learn to write a short macro that processes raw data and outputs results automatically.
  1. Demo script (written in Jython)
  2. Student ImageJ macro: example
documents/121019htm.txt · Last modified: 2016/05/24 12:46 (external edit)