A Domain-Adaptive Two-Stream U-Net For Electron Microscopy Image Segmentation

Deep networks such as the U-Net are outstanding at segmenting biomedical images when enough training data is available, but only then. Here we introduce a Domain Adaptation approach that relies on two coupled U-Nets that either regularize or share corresponding weights between the two streams, along with a differentiable loss function that approximates the Jaccard index, to leverage training data from one domain in which it is plentiful, to adapt the network weights in another where it is scarce. We showcase our approach for the purpose of segmenting mitochondria and synapses from electron microscopy image stacks of mouse brain, when we have enough training data for one brain region but only very little for another. In such cases, we outperform state-of-the-art Domain Adaptation methods.


Published in:
International Symposium On Biomedical Imaging (ISBI), 400-404
Presented at:
International Symposium on Biomedical Imaging (ISBI), Washington, DC, Apr 04-07, 2018
Year:
Jan 01 2018
Publisher:
New York, IEEE
ISSN:
1945-7928
ISBN:
978-1-5386-3636-7
Keywords:
Laboratories:




 Record created 2019-01-25, last modified 2019-08-12

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