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Open Access Highly Accessed Methodology article

Unsupervised segmentation of noisy electron microscopy images using salient watersheds and region merging

Saket Navlakha1*, Parvez Ahammad2* and Eugene W Myers3

Author Affiliations

1 School of Computer Science, Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA

2 Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, VA, USA

3 Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

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BMC Bioinformatics 2013, 14:294  doi:10.1186/1471-2105-14-294

Published: 4 October 2013



Segmenting electron microscopy (EM) images of cellular and subcellular processes in the nervous system is a key step in many bioimaging pipelines involving classification and labeling of ultrastructures. However, fully automated techniques to segment images are often susceptible to noise and heterogeneity in EM images (e.g. different histological preparations, different organisms, different brain regions, etc.). Supervised techniques to address this problem are often helpful but require large sets of training data, which are often difficult to obtain in practice, especially across many conditions.


We propose a new, principled unsupervised algorithm to segment EM images using a two-step approach: edge detection via salient watersheds following by robust region merging. We performed experiments to gather EM neuroimages of two organisms (mouse and fruit fly) using different histological preparations and generated manually curated ground-truth segmentations. We compared our algorithm against several state-of-the-art unsupervised segmentation algorithms and found superior performance using two standard measures of under-and over-segmentation error.


Our algorithm is general and may be applicable to other large-scale segmentation problems for bioimages.

Image segmentation; Superpixels; Salient watershed; Region merging; Electron microscopy; Unsupervised learning