Work

Identifying components in 3D density maps of protein nanomachines by multi-scale segmentation

Year: 2009

Type: conference-paper

Abstract: Segmentation of density maps obtained using cryo-electron microscopy (cryo-EM) is a challenging task, and is typically accomplished by time-intensive interactive methods. The goal of segmentation is to identify the regions inside the density map that correspond to individual components. We present a multi-scale segmentation method for accomplishing this task that requires very little user interaction. The method uses the concept of scale space, which is created by convolution of the input density map with a Gaussian filter. The latter process smoothes the density map. The standard deviation of the Gaussian filter is varied, with smaller values corresponding to finer scales and larger values to coarser scales. Each of the maps at different scales is segmented using the watershed method, which is very efficient, completely automatic, and does not require the specification of seed points. Some detail is lost in the smoothing process. A sharpening process reintroduces detail into the segmentation at the coarsest scale by using the segmentations at the finer scales. We apply the method to simulated density maps, where the exact segmentation (or ground truth) is known, and rigorously evaluate the accuracy of the resulting segmentations.

Source: IEEE/NIH Life Science Systems and Applications Workshop

DOI: 10.1109/lissa.2009.4906705

Authors: Grigore Pintilie, Junjie Zhang, Wah Chiu, David Gossard

Institutions: Massachusetts Institute of Technology, Baylor University, Baylor College of Medicine

Language: English

Topic: Advanced Electron Microscopy Techniques and Applications

Subfield: Structural Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green