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advert_Fiji_advanced.html
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<!DOCTYPE html>
<html>
<head>
<title>Fiji basics</title>
<meta charset="UTF-8">
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<a href="javascript:void(0)" class="w3-button cci-blue w3-margin-right" onclick="w3_open()"><i class="fa fa-bars" style="margin-right:10px;"></i> <!-- <img style="height:30px;margin-bottom:-6px;" src="img/logo_dark_sq.png"/> -->
<span class="titleLong">Image Analysis Training Courses and more</span><span class="titleShort">Image Analysis courses</span></a>
</header>
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<h1 class="w3-xlarge cci-text-blue"><b>Fiji Basics</b></h1>
<hr class="cci-hr">
<img class="equ_img" src="img/scopes_dragonfly.png" alt="Image?" />
<div class="">
<h3>Summary</h3>
<p>Fiji is an open source software for processing and analysing scientific images. This course will cover ..... There will be a focus on practical sessions where the participants will gain hands-on experience. </p>
<h3>Target Audience</h3>
<ul >
<li>All researchers using light microscopy</li>
<li>Those with some experience in bioimage analysis, e.g. self-taught or those who attended the Fiji Basics course</li>
<li class="last">Students and staff members from the universities in Liverpool, affiliated institutions and other external institutions/individuals</li>
</ul>
<h3>Pre-requisites</h3>
<ul >
<li>Regular user of Fiji </li>
<li>Confidently adjust and annotate images </li>
<li>Working knowledge of a typical image analysis workflow </li>
<li>The desire to learn about multidimensional image analysis</li>
<li>The desire to use code-free shallow machine learning-based segmentation</li>
</ul>
<h3>Aims and Objectives</h3>
<p>This course will enable participants to: </p>
<ul>
<li>Recall where and how to access additional help in the future</li>
<li>Identify the place of image analysis within the experimental pipeline</li>
<li>Describe the formation of digital microscopy images</li>
<li>Practise installing Fiji and keeping it up to date</li>
<li>Recognise common image properties</li>
<li>Employ image adjustments and annotation tools</li>
<li>Explain a typical image analysis sequence</li>
<li>Propose image pre-processing to reduce noise and enhance features</li>
<li>Set up measurements</li>
<li>Propose an image analysis pipeline for a given task</li>
<li>Recreate an image analysis pipeline from the literature</li>
<li>Evaluate a given image analysis pipeline</li>
<li class="last">Recall the FAIR principle</li>
</ul>
<h3>Course Materials</h3>
<p>This material was adapted from a course run in the CCI by David Mason in 2018: </p>
<ul>
<li>Lecture slides</li>
<li>Exercises</li>
<li>Sample Data</li>
<li class="last">Course work (optional though strongly encouraged)</li>
</ul>
<h3>Administrative Details: </h3>
<ul>
<li>Duration: TBA</li>
<li>Pricing: TBA</li>
</ul>
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<a target="_blank" href="http://www.liv.ac.uk"><img src="img/uni_logo_new.svg" alt="University of Liverpool" /></a>
<p style="margin-left:5px;clear:both;">© Marie Held</p>
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