Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms

Bo Zhou, Qiang Guo, Kaiwen Wang, Xiangrui Zeng, Xin Gao, Min Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Electron Cryo-Tomography (ECT) allows 3D visualization of subcellular structures at the submolecular resolution in close to the native state. However, due to the high degree of structural complexity and imaging limits, the automatic segmentation of cellular components from ECT images is very difficult. To complement and speed up existing segmentation methods, it is desirable to develop a generic cell component segmentation method that is 1) not specific to particular types of cellular components, 2) able to segment unknown cellular components, 3) fully unsupervised and does not rely on the availability of training data. As an important step towards this goal, in this paper, we propose a saliency detection method that computes the likelihood that a subregion in a tomogram stands out from the background. Our method consists of four steps: supervoxel over-segmentation, feature extraction, feature matrix decomposition, and computation of saliency. The method produces a distribution map that represents the regions' saliency in tomograms. Our experiments show that our method can successfully label most salient regions detected by a human observer, and able to filter out regions not containing cellular components. Therefore, our method can remove the majority of the background region, and significantly speed up the subsequent processing of segmentation and recognition of cellular components captured by ECT.
Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Print)9781538654880
StatePublished - Feb 28 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank Dr. Robert F. Murphy for suggestions. This work was supported in part by U.S. National Institutes of Health (NIH) grant P41 GM103712. MX acknowledge support from Samuel and Emma Winters Foundation.


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