# From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging

arXiv: https://arxiv.org/abs/2505.11394 · HTML: https://mlsanity.com/paper/2505.11394/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 78 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Distribution Matching Losses Can Hallucinate Features in Medical Image Translation](https://mlsanity.com/paper/1805.08841/) (2018)
- [Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue](https://mlsanity.com/paper/1803.11293/) (2018)
- [Deep learning-based transformation of the H&E stain into special stains](https://mlsanity.com/paper/2008.08871/) (2020)
- [PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning](https://mlsanity.com/paper/1807.07701/) (2018)
- [Contour Proposal Networks for Biomedical Instance Segmentation](https://mlsanity.com/paper/2104.03393/) (2021)
- [Virtual stain transfer in histology via cascaded deep neural networks](https://mlsanity.com/paper/2207.06578/) (2022)
- [Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections](https://mlsanity.com/paper/2011.12865/) (2020)
- [Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks](https://mlsanity.com/paper/1705.10545/) (2017)
- [Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI](https://mlsanity.com/paper/2401.17207/) (2024)
- [Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale](https://mlsanity.com/paper/2011.12857/) (2020)
- [Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain](https://mlsanity.com/paper/2311.16821/) (2023)

## Abstract (arXiv)

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, a method that is label-free and allows subsequent staining of sections after 3D-PLI measurement. By post-staining for cell bodies, a direct link between fiber- and cytoarchitecture can potentially be established in the same section. However, inevitable distortions introduced during the staining process make a costly 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 such 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. We use a supervised setting, building 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. 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 can predict a Cresyl violet staining from 3D-PLI, resulting in a virtual staining that exhibits plausible patterns of cell organization in gray matter, with larger cell bodies being localized at their expected positions.