# Domain Adaptive Segmentation in Volume Electron Microscopy Imaging

arXiv: https://arxiv.org/abs/1810.09734 · HTML: https://mlsanity.com/paper/1810.09734/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2018.

## Built on by (most cited first, 9 of 9)

- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2310.20271/) (2023)
- [WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy](https://mlsanity.com/paper/2210.13109/) (2022)
- [Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation](https://mlsanity.com/paper/2404.00667/) (2024)
- [Deep learning based domain adaptation for mitochondria segmentation on EM volumes](https://mlsanity.com/paper/2202.10773/) (2022)
- [FreeCOS: Self-Supervised Learning from Fractals and Unlabeled Images for Curvilinear Object Segmentation](https://mlsanity.com/paper/2307.07245/) (2023)
- [SelfAdapt: Unsupervised Domain Adaptation of Cell Segmentation Models](https://mlsanity.com/paper/2508.11411/) (2025)
- [μMatch: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM](https://mlsanity.com/paper/2606.21605/) (2026)

## Abstract (arXiv)

In the last years, automated segmentation has become a necessary tool for volume electron microscopy (EM) imaging. So far, the best performing techniques have been largely based on fully supervised encoder-decoder CNNs, requiring a substantial amount of annotated images. Domain Adaptation (DA) aims to alleviate the annotation burden by 'adapting' the networks trained on existing groundtruth data (source domain) to work on a different (target) domain with as little additional annotation as possible. Most DA research is focused on the classification task, whereas volume EM segmentation remains rather unexplored. In this work, we extend recently proposed classification DA techniques to an encoder-decoder layout and propose a novel method that adds a reconstruction decoder to the classical encoder-decoder segmentation in order to align source and target encoder features. The method has been validated on the task of segmenting mitochondria in EM volumes. We have performed DA from brain EM images to HeLa cells and from isotropic FIB/SEM volumes to anisotropic TEM volumes. In all cases, the proposed method has outperformed the extended classification DA techniques and the finetuning baseline. An implementation of our work can be found on https://github.com/JorisRoels/domain-adaptive-segmentation.