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Abstract #0832

Segmentation-aware adversarial synthesis for registration of histology to MRI

Matteo Mancini1, Yuankai Huo2, Bennett A. Landman2, and Juan Eugenio Iglesias1

1Centre for Medical Image Computing, Dept. of Med. Phys. and Biomed. Eng., University College London, London, United Kingdom, 2Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States

MRI-histology registration lays the ground for a new generation of high-resolution brain atlases. The task is challenging given the different contrast and the histology-related artifacts. We propose a dataset-specific, synthesis-based approach that uses a generative adversarial network to reduce the problem to intra-modality registration. Exploiting automatic segmentation data and cycle-consistency, the proposed architecture is suitable for small-size datasets. We show the advantages of this approach compared to canonical registration both in quantitative and qualitative terms using data from the Allen Institute’s Human Brain Atlas.

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