@inproceedings{c2f850cd132c483d906a977350bd780f,
title = "Automatic image registration of optoacoustic tomography and magnetic resonance imaging based on deep learning",
abstract = "Multi-spectral optoacoustic tomography (MSOT) combines rich contrast of optical imaging and high resolution of ultrasound, and becomes an attractive biomedical research tool in the last decade. Aligning MSOT images with anatomical map provided by magnetic resonance imaging (MRI) can potentially enhance the interpretation of optoacoustic signal which mainly reflects molecular and functional information. Therefore, developing an automated algorithm of image registration between MSOT and MRI is crucial. Existing MSOT-MRI registration algorithms mostly relied on manual segmentation, which requires user-dependent experience. Herein, we developed a fully automated algorithm for MSOT-MRI registration based on deep learning (DL). This workflow consists of DL-based segmentation and image transformation. We have experimentally demonstrated the accuracy and computational efficiency of the method, paving the way towards high-throughput MSOT data analysis in close future.",
keywords = "Image registration, brain imaging, deep learning, magnetic resonance imaging, optoacoustic tomography",
author = "Yexing Hu and Berkan Lafci and Artur Luzgin and Hao Wang and Jan Klohs and De{\'a}n-Ben, {Xose Luis} and Ruiqing Ni and Daniel Razansky and Wuwei Ren",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE. All rights reserved.; 1st Conference on Biomedical Photonics and Cross-Fusion, BPC 2022 ; Conference date: 21-08-2022 Through 23-08-2022",
year = "2022",
doi = "10.1117/12.2655214",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Zhenxi Zhang and Junle Qu and Buhong Li",
booktitle = "1st Conference on Biomedical Photonics and Cross-Fusion, BPC 2022",
}