From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging
Journal:
arXiv
Published Date:
May 16, 2025
Abstract
Comprehensive assessment of the various aspects of the brain's microstructure
requires the use of complementary imaging techniques. This includes measuring
the spatial distribution of cell bodies (cytoarchitecture) and nerve fibers
(myeloarchitecture). The gold standard for cytoarchitectonic analysis is light
microscopic imaging of cell-body stained tissue sections. To reveal the 3D
orientations of nerve fibers, 3D Polarized Light Imaging (3D-PLI) has been
introduced as a reliable technique providing a resolution in the micrometer
range while allowing processing of series of complete brain sections. 3D-PLI
acquisition is label-free and allows subsequent staining of sections after
measurement. By post-staining for cell bodies, a direct link between fiber- and
cytoarchitecture can potentially be established within the same section.
However, inevitable distortions introduced during the staining process make a
nonlinear and cross-modal registration necessary in order to study the detailed
relationships between cells and fibers in the images. In addition, the
complexity of processing histological sections for post-staining only allows
for a limited number of samples. In this work, we take advantage of deep
learning methods for image-to-image translation to generate a virtual staining
of 3D-PLI that is spatially aligned at the cellular level. In a supervised
setting, we build on a unique dataset of brain sections, to which Cresyl violet
staining has been applied after 3D-PLI measurement. To ensure high
correspondence between both modalities, we address the misalignment of training
data using Fourier-based registration methods. In this way, registration can be
efficiently calculated during training for local image patches of target and
predicted staining. We demonstrate that the proposed method enables prediction
of a Cresyl violet staining from 3D-PLI, matching individual cell instances.
