Improved deep learning based macromolecules structure classification from electron cryo tomograms
| Autor: | Xiangrui Zeng, Ruogu Lin, Karim Elmaaroufi, Min Xu, John Galeotti, Chengqian Che |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
030102 biochemistry & molecular biology Artificial neural network Contextual image classification Computer science business.industry Deep learning Pattern recognition Residual Quantitative Biology - Quantitative Methods Article Computer Science Applications 03 medical and health sciences 030104 developmental biology Hardware and Architecture FOS: Biological sciences Pattern recognition (psychology) Imaging technology Computer Vision and Pattern Recognition Artificial intelligence business Focus (optics) Quantitative Methods (q-bio.QM) Software Block (data storage) |
| Popis: | Cellular processes are governed by macromolecular complexes inside the cell. Study of the native structures of macromolecular complexes has been extremely difficult due to lack of data. With recent breakthroughs in Cellular electron cryo tomography (CECT) 3D imaging technology, it is now possible for researchers to gain accesses to fully study and understand the macromolecular structures single cells. However, systematic recovery of macromolecular structures from CECT is very difficult due to high degree of structural complexity and practical imaging limitations. Specifically, we proposed a deep learning based image classification approach for large-scale systematic macromolecular structure separation from CECT data. However, our previous work was only a very initial step towards exploration of the full potential of deep learning based macromolecule separation. In this paper, we focus on improving classification performance by proposing three newly designed individual CNN models: an extended version of (Deep Small Receptive Field) DSRF3D, donated as DSRF3D-v2, a 3D residual block based neural network, named as RB3D and a convolutional 3D(C3D) based model, CB3D. We compare them with our previously developed model (DSRF3D) on 12 datasets with different SNRs and tilt angle ranges. The experiments show that our new models achieved significantly higher classification accuracies. The accuracies are not only higher than 0.9 on normal datasets, but also demonstrate potentials to operate on datasets with high levels of noises and missing wedge effects presented. Preliminary working report |
| Databáze: | OpenAIRE |
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