Domain Randomization for Macromolecule Structure Classification and Segmentation in Electron Cyro-tomograms
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group
Permanent link to this recordhttp://hdl.handle.net/10754/663480
MetadataShow full item record
AbstractIt is crucial to study and understand cellular processes. In recent years, Cellular Electron CryoTomography (CECT) serves as a powerful 3D imaging tool to visualize spatial structure of macromolecules inside the cell. However, it is challenging to analyze the macromolecular structures in a systematic way due to nature of the structural complexity of subcellular components. Existing computational and deep learning based approaches suffer from limited scalability, discrimination ability and lack of accurate annotated CECT data. Training with cheap simulated data can alleviate this problem while facing new challenges of bridging the 'reality gap' between synthetic training data and real testing data. In this paper, we tackle the tasks of macromolecule structure classification and segmentation in CECT images by adapting a simple but effective technique, domain randomization. We show that by combining deep neural models and domain randomization, we are able to achieve significant improvements of 35.21% and 46.34% in tasks of classification and semantic segmentation for real CECT data, comparing to the model trained only on syhthetic data that aims to faithfully reproduce real-world data distribution.
CitationChe, C., Xian, Z., Zeng, X., Gao, X., & Xu, M. (2019). Domain Randomization for Macromolecule Structure Classification and Segmentation in Electron Cyro-tomograms. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). doi:10.1109/bibm47256.2019.8983110
SponsorsThis work was supported in part by U.S. National Institutes of Health (NIH) grant P41 GM103712. XZ was supported by a fellowship from Carnegie Mellon University’s Center for Machine Learning and Health. XG was supported by the King Abdullah University of Science and Technology.
Conference/Event name2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019