Slice to volume registration using neural networks for serial optical coherence tomography of whole mouse brains

Frans Irgolitsch; François Huppé-Marcoux; Frédéric Lesage; Joël Lefebvre

doi:10.1117/12.3002557

13 March 2024 Slice to volume registration using neural networks for serial optical coherence tomography of whole mouse brains

Frans Irgolitsch, François Huppé-Marcoux, Frédéric Lesage, Joël Lefebvre

Author Affiliations +

Proceedings Volume 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; 128570K (2024) https://doi.org/10.1117/12.3002557
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Optical coherence tomography (OCT) has shown an affinity for imaging white matter using intrinsic signals. Combined with an automated vibratome and mosaic imaging, serial blockface histology (SBH) can yield whole-brain white matter images at high resolution. A current drawback of SBH is the lack of real-time information that complicates the localization of brain structures during imaging. To address this, imaged slices can be registered to a pre-existing 3D volume to provide more contextual information during the acquisition. 3D brain image registration is a process where a volume is aligned to a standard template to perform further analysis in a common reference frame. Without a full 3D volume, however, this slice-to-volume registration often proves difficult. The search space is large, and the limited information hampers existing algorithms. In this article, we present a neural network that predicts the 3D position of a 2D slice and aligns it to the corresponding slice in 3D template volume. The network uses a VGG16 backbone to extract features, followed by fully connected layers to predict the transformations. Six mouse brains at a resolution of 25μm, imaged using Serial OCT, have been used to train the network. The loss is calculated by taking the Euclidian distance between the predictions and the ground truth, which has been randomly sampled from the volume. Applications for this model are 2D to 3D slice registration, providing contextual information during serial OCT acquisitions such as the progress, or a parcelization of the current slice into its brain regions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Frans Irgolitsch, François Huppé-Marcoux, Frédéric Lesage, and Joël Lefebvre "Slice to volume registration using neural networks for serial optical coherence tomography of whole mouse brains", Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 128570K (13 March 2024); https://doi.org/10.1117/12.3002557

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