3 research outputs found
Domain adaptive segmentation in volume electron microscopy imaging
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
Domain Adaptive Segmentation in Volume Electron Microscopy Imaging
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.Comment: ISBI 2019 (accepted