When deep learning meets image registration

 A computer vision technique inspired by the biological brain

 The class of deep-learning models Dr. Enzo Ferrante aims to apply to image registration are called deep convolutional neural networks (CNNs). As the term “neural network” suggests, these computational models are inspired by biological brains and can perform complex feats of intelligence, while using little pre-processing compared to other machine learning algorithms. Still, to reduce the amount of image data necessary for training such a network, Dr. Enzo Ferrante intends to incorporate some prior-knowledge into the CNN-based image registration model. His idea is to go beyond simple visual observations and introduce context coming from prior knowledge such as anatomy.

 Images can contain hidden information crucial to environmental and life danger. In this sense, accurate registration algorithms are essential in supporting Earth and medical scientists in their studies involving image aggregation and comparison. Research on diseases that remain largely mysterious, like Alzheimer for instance, could greatly benefit from the gains in terms of accuracy and computational time aimed in Dr. Enzo Ferrante’s project. Similarly, his innovative image registration methods and toolboxes will likely contribute to a better image-based understanding of Climate change and its consequences on the Earth’s surface.