Domain Adaptive Segmentation in Volume Electron Microscopy Imaging

TL;DR

This paper introduces a novel domain adaptation method for volume electron microscopy segmentation, improving accuracy across different EM imaging modalities with minimal additional annotation.

Contribution

It extends classification domain adaptation techniques to encoder-decoder segmentation models by adding a reconstruction decoder for better feature alignment.

Findings

Outperforms existing DA techniques in EM segmentation tasks.

Effective across different EM imaging modalities and datasets.

Reduces annotation effort for new EM domains.

Abstract

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.