Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging
METADATA ONLY
Loading...
Author / Producer
Date
2022-09-01
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Multispectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) which offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of MSOT-MRI images remains challenging, chiefly due to the entirely different image contrast rendered by these two modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. The performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
13 (9)
Pages / Article No.
4817 - 4833
Publisher
Optica
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09648 - Razansky, Daniel / Razansky, Daniel