# Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks

arXiv: https://arxiv.org/abs/1704.01510 · HTML: https://mlsanity.com/paper/1704.01510/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2017.

## Built on by (most cited first, 17 of 17)

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- [Interpretable Unsupervised Diversity Denoising and Artefact Removal](https://mlsanity.com/paper/2104.01374/) (2021)
- [DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images](https://mlsanity.com/paper/2306.12109/) (2023)
- [Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy](https://mlsanity.com/paper/2104.09435/) (2021)
- [N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture](https://mlsanity.com/paper/2211.08512/) (2022)
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- [Reference-Free Isotropic 3D EM Reconstruction using Diffusion Models](https://mlsanity.com/paper/2308.01594/) (2023)
- [μSplit: efficient image decomposition for microscopy data](https://mlsanity.com/paper/2211.12872/) (2022)
- [denoiSplit: a method for joint microscopy image splitting and unsupervised denoising](https://mlsanity.com/paper/2403.11854/) (2024)
- [Reference-free Axial Super-resolution of 3D Microscopy Images using Implicit Neural Representation with a 2D Diffusion Prior](https://mlsanity.com/paper/2408.08616/) (2024)
- [MR Slice Profile Estimation by Learning to Match Internal Patch Distributions](https://mlsanity.com/paper/2104.00100/) (2021)
- [Self-Supervised Super-Resolution Approach for Isotropic Reconstruction of 3D Electron Microscopy Images from Anisotropic Acquisition](https://mlsanity.com/paper/2309.10646/) (2023)
- [Self-Supervised Isotropic Superresolution Fetal Brain MRI](https://mlsanity.com/paper/2211.06502/) (2022)
- [Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning](https://mlsanity.com/paper/2303.15214/) (2023)
- [DeepContrast: Deep Tissue Contrast Enhancement using Synthetic Data Degradations and OOD Model Predictions](https://mlsanity.com/paper/2308.08365/) (2023)
- [SkelEM: Training-Signal Decoupling of Skeleton and Diffusion for Self-supervised Axial Super-Resolution in Volume Microscopy](https://mlsanity.com/paper/2606.30012/) (2026)
- [Beyond Isotropic Assumptions: Continuity-Constrained Segmentation and GPU Morphometry for Nanoscale GBM Analysis](https://mlsanity.com/paper/2608.07575/) (2026)

## Abstract (arXiv)

Fluorescence microscopy images usually show severe anisotropy in axial versus lateral resolution. This hampers downstream processing, i.e. the automatic extraction of quantitative biological data. While deconvolution methods and other techniques to address this problem exist, they are either time consuming to apply or limited in their ability to remove anisotropy. We propose a method to recover isotropic resolution from readily acquired anisotropic data. We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the network to. The network effectively learns to restore the full isotropic resolution by restoring the image under a trained, sample specific image prior. We apply our method to 3 synthetic and 3 real datasets and show that our results improve on results from deconvolution and state-of-the-art super-resolution techniques. Finally, we demonstrate that a standard 3D segmentation pipeline performs on the output of our network with comparable accuracy as on the full isotropic data.