Semi-supervised Image-to-Image translation for robust image registration

henrik skibbe, akiya watakabe, Febrian Rachmadi, Carlos Enrique Gutierrez, Ken Nakae, tetsuo yamamori

Keywords: Semi-supervised image-to-image translation, GAN, Image registration

TL;DR: A semi-supervised image-to-image translation made possible an automated, reliable multi-modal image registration of thousands of microscopy marmoset brain images

Abstract: The Japan Brain/MINDS Project aims at studying the neural networks controlling higher brain functions in the marmoset. As part of it, we develop an image processing pipeline for marmoset brain imaging data, where various microscopy images of different modalities need to be co-registered. In initial experiments, multi-modal image registration frequently failed due to an erroneous initialization. Our data set includes images of Nissl stained brain sections, backlit images as well as images of neural tracer injections using two-photon microscopy. More than 10000 high-resolution 2D images required co-registration, a large amount that demands a reliable automation process. We implemented a semi-supervised image-to-image translation which allowed a robust image alignment initialization. With such an initial alignment, all images can be successfully registered using a state-of-the-art multi-modal image registration algorithm.

Paper Type: validation/application paper

Primary Subject Area: Image Registration

Secondary Subject Area: Transfer Learning and Domain Adaptation

Paper Status: original work, not submitted yet

Source Code Url: bitbucket.org/skibbe/midl2021_henrik

Data Set Url: Data will be made publicly available in 2021/ 2022

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