Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms

TL;DR

This paper introduces an unsupervised saliency detection method for Electron Cryo-Tomography images, which identifies prominent cellular regions to facilitate faster and more generic segmentation of subcellular structures.

Contribution

It proposes a novel feature decomposition-based saliency detection approach that is unsupervised, generic, and capable of identifying unknown cellular components in ECT images.

Findings

Successfully labels salient regions matching human observation

Effectively filters out background regions

Speeds up subsequent cellular component segmentation

Abstract

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.