Imaging inside living beings is important to understand how life develops, how healthy bodies work, and how diseases begin and progress. This demands specialized equipment and knowledge. AMICE will develop such techniques and bring them into a nationwide preclinical infrastructure. This will help researchers acquire more information. In addition, AMICE will promote re-use of images to increase efficiency in science. Through this AMICE will help Dutch life scientists revolutionize their research with unique and innovative imaging techniques and to stay at the top of the international science community.

Animal research direly needs an open science revolution! Traditional barriers for sharing data (i.e. privacy concerns) do not apply to data collected in animals. Still, the open science mindset hasn’t permeated the animal research community. We are actively changing this. To open up animal research, we will aggregate and curate datasets from multiple animal imaging centers worldwide, and distribute open software for preprocessing. To enable laboratories to engage in open science practices, we will organize workshops to transfer technical know-how to our research community. By leading by example, we hope to inspire the next generation of animal researchers.

The Dutch National 14Tesla Initiative in Medical Science will establish the first MRI system at this field strength in the world. It will provide a national and international resource that will help cement the leading position of the Netherlands in ultra-high field imaging techniques and their application in Medicine and Neuroscience. The additional sensitivity afforded by the system combined with the improved ability to discern metabolite signals will offer scientists a powerful instrument for better characterization of a range of diseases and new insights into the workings of the brain.

In this project, we ask teams of students to take part in four running scientific medical image analysis challenges. In the project, they will apply knowledge and skills in image analysis and machine learning acquired in the first part of the course, especially deep learning technology. Our goal is that the students become familiar with deep learning techniques used in medical image analysis and that they learn to apply and compare the effectiveness of different approaches in a challenging real-world problem.