Navlakha, S., Ahammad, P., Myers, E. W. (October 2013) Unsupervised segmentation of noisy electron microscopy images using salient watersheds and region merging. BMC Bioinformatics, 14. p. 294. ISSN 1471-2105
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Abstract
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: Our algorithm is general and may be applicable to other large-scale segmentation problems for bioimages.
| Item Type: | Paper |
|---|---|
| Subjects: | organism description > animal > insect > Drosophila bioinformatics > computational biology > algorithms Investigative techniques and equipment > microscopy > electron microscopy organism description > animal > mammal > rodent > mouse |
| CSHL Authors: | |
| Communities: | CSHL labs > Navlakha lab |
| Depositing User: | Matthew Dunn |
| Date: | 4 October 2013 |
| Date Deposited: | 08 Nov 2019 14:10 |
| Last Modified: | 08 Nov 2019 14:10 |
| PMCID: | PMC3852992 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/38691 |
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