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Erschienen in:
2016 | OriginalPaper | Buchkapitel
SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation
verfasst von : Ting Liu, Miaomiao Zhang, Mehran Javanmardi, Nisha Ramesh, Tolga Tasdizen
Erschienen in: Computer Vision – ECCV 2016
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Abstract
Region-based methods have proven necessary for improving segmentation accuracy of neuronal structures in electron microscopy (EM) images. Most region-based segmentation methods use a scoring function to determine region merging. Such functions are usually learned with supervised algorithms that demand considerable ground truth data, which are costly to collect. We propose a semi-supervised approach that reduces this demand. Based on a merge tree structure, we develop a differentiable unsupervised loss term that enforces consistent predictions from the learned function. We then propose a Bayesian model that combines the supervised and the unsupervised information for probabilistic learning. The experimental results on three EM data sets demonstrate that by using a subset of only \(3\,\%\) to \(7\,\%\) of the entire ground truth data, our approach consistently performs close to the state-of-the-art supervised method with the full labeled data set, and significantly outperforms the supervised method with the same labeled subset.