Watershed merge tree classification for electron microscopy image segmentation

Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for t...

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Bibliographic Details
Main Authors: Ting Liu, Jurrus, E., Seyedhosseini, M., Ellisman, M., Tasdizen, T.
Format: Conference Proceeding
Language:English
Subjects:
Electron microscopy
Feature extraction
Image segmentation
Merging
Neurons
Vegetation
Online Access:Request full text
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Summary:Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier is learned with non-local image features to predict each potential merge in the tree, upon which merge decisions are made with consistency constraints to acquire the final segmentation. Independent of classifiers and decision strategies, our approach proposes a general framework for efficient hierarchical segmentation with statistical learning. We demonstrate that our method leads to a substantial improvement in segmentation accuracy.
ISSN:1051-4651
2831-7475