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Translating Light-Sheet Microscopy Images to Virtual H&E Using CycleGAN

Yanhua Zhao

TL;DR

This work tackles translating fluorescence light-sheet microscopy into histology-like H&E images to bridge two modalities used in pathology. It adopts a CycleGAN-based unpaired image-to-image translation, combining two fluorescence channels into RGB and learning bidirectional mappings while preserving morphological structure. The approach demonstrates visually realistic virtual H&E outputs and discusses integration with existing H&E workflows, though quantitative validation is limited by the absence of paired ground truth. Key limitations include a small fluorescence training set and potential artifacts, with suggested future directions spanning larger datasets, objective evaluation, and alternative unpaired methods to enhance robustness.

Abstract

Histopathology analysis relies on Hematoxylin and Eosin (H&E) staining, but fluorescence microscopy offers complementary information. Converting fluorescence images to H&E-like appearance can aid interpretation and integration with standard workflows. We present a Cycle-Consistent Adversarial Network (CycleGAN) approach for unpaired image-to-image translation from multi-channel fluorescence microscopy to pseudo H&E stained histopathology images. The method combines C01 and C02 fluorescence channels into RGB and learns a bidirectional mapping between fluorescence and H&E domains without paired training data. The architecture uses ResNet-based generators with residual blocks and PatchGAN discriminators, trained with adversarial, cycle-consistency, and identity losses. Experiments on fluorescence microscopy datasets show the model generates realistic pseudo H&E images that preserve morphological structures while adopting H&E-like color characteristics. This enables visualization of fluorescence data in a format familiar to pathologists and supports integration with existing H&E-based analysis pipelines.

Translating Light-Sheet Microscopy Images to Virtual H&E Using CycleGAN

TL;DR

This work tackles translating fluorescence light-sheet microscopy into histology-like H&E images to bridge two modalities used in pathology. It adopts a CycleGAN-based unpaired image-to-image translation, combining two fluorescence channels into RGB and learning bidirectional mappings while preserving morphological structure. The approach demonstrates visually realistic virtual H&E outputs and discusses integration with existing H&E workflows, though quantitative validation is limited by the absence of paired ground truth. Key limitations include a small fluorescence training set and potential artifacts, with suggested future directions spanning larger datasets, objective evaluation, and alternative unpaired methods to enhance robustness.

Abstract

Histopathology analysis relies on Hematoxylin and Eosin (H&E) staining, but fluorescence microscopy offers complementary information. Converting fluorescence images to H&E-like appearance can aid interpretation and integration with standard workflows. We present a Cycle-Consistent Adversarial Network (CycleGAN) approach for unpaired image-to-image translation from multi-channel fluorescence microscopy to pseudo H&E stained histopathology images. The method combines C01 and C02 fluorescence channels into RGB and learns a bidirectional mapping between fluorescence and H&E domains without paired training data. The architecture uses ResNet-based generators with residual blocks and PatchGAN discriminators, trained with adversarial, cycle-consistency, and identity losses. Experiments on fluorescence microscopy datasets show the model generates realistic pseudo H&E images that preserve morphological structures while adopting H&E-like color characteristics. This enables visualization of fluorescence data in a format familiar to pathologists and supports integration with existing H&E-based analysis pipelines.
Paper Structure (9 sections, 4 figures)

This paper contains 9 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Method Selection for Virtual Staining
  3. Data Preparation and Preprocessing
  4. Model Architecture
  5. Generator Architecture
  6. Discriminator Architecture
  7. Cycle-Consistent Architecture
  8. Results
  9. Limitations and Possible Improvements

Figures (4)

  • Figure 1: Example H&E images from the MHIST dataset b3. Each image shows different tissue regions used as the reference H&E domain in CycleGAN training.
  • Figure 2: Overview of the proposed virtual H&E generation pipeline. A is Source Domain, B is Target Domain.
  • Figure 3: Virtual H&E generation from light-sheet microscopy. Columns show TO-PRO-3 (nuclear), Eusion, and CycleGAN generated virtual H&E images. Each row corresponds to a different slice.Results are shown from the CycleGAN model at epoch .
  • Figure 4: Comparison of CycleGAN generated virtual H&E images across training epochs. Columns show outputs generated at different training epochs, while each row corresponds to the same tissue slice.