Apr 9, 2020

Subcellular structure segmentation from cryo-electron tomograms via machine learning

BioRxiv : the Preprint Server for Biology
Chao YangN. K. Sauter

Abstract

We describe how to use several machine learning techniques organized in a learning pipeline to segment and identify subcellular structures from cryo-electron tomograms. These tomograms are difficult to analyze with traditional segmentation tools. The learning pipeline in our approach starts from supervised learning via a special convolutional neural network trained with simulated data. It continues with semi-supervised reinforcement learning and/or a region merging techniques that try to piece together disconnected components that should belong to the same subcellular structure. A parametric or non-parametric fitting procedure is then used to enhance the segmentation results and quantify uncertainties in the fitting. Domain knowledge is used in generating the training data for the neural network and in guiding the fitting procedure through the use of appropriately chosen priors and constraints. We demonstrate that the approach proposed here work well for extracting membrane surfaces of protein reconstituted liposomes in a cellular environment that contains other artifacts.

  • References
  • Citations

References

  • We're still populating references for this paper, please check back later.
  • References
  • Citations

Citations

  • This paper may not have been cited yet.

Mentioned in this Paper

Pachyneurum grandiflorum
Protein Methylation
Genome
Genes
Cistanthe grandiflora
Coursetia grandiflora
Tissue Specificity
Methylate
Clermontia grandiflora
Cineraria grandiflora

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

Journal of Digital Imaging
Bradley J EricksonKenneth Philbrick
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Alberto BartesaghiSriram Subramaniam
Journal of Structural Biology
Rajesh NarasimhaSriram Subramaniam
© 2020 Meta ULC. All rights reserved