Deep Learning Based Supervised Semantic Segmentation of Electron Cryo-Subtomograms
| Autor: | Zachary Freyberg, Eric P. Xing, Xiaodan Liang, Min Xu, Ruogu Lin, Xiangrui Zeng, Chang Liu |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
0301 basic medicine business.industry Computer science Generalization Computer Vision and Pattern Recognition (cs.CV) Deep learning Machine Learning (stat.ML) Pattern recognition Image segmentation Convolutional neural network Visualization 03 medical and health sciences 030104 developmental biology FOS: Biological sciences Segmentation Artificial intelligence business Quantitative Methods (q-bio.QM) |
| Zdroj: | ICIP |
| DOI: | 10.1109/icip.2018.8451386 |
| Popis: | Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution. It enables analyzing the native structures of macromolecular complexes and their spatial organization inside single cells. However, due to the high degree of structural complexity and practical imaging limitations, systematic macromolecular structural recovery inside CECT images remains challenging. Particularly, the recovery of a macromolecule is likely to be biased by its neighbor structures due to the high molecular crowding. To reduce the bias, here we introduce a novel 3D convolutional neural network inspired by Fully Convolutional Network and Encoder-Decoder Architecture for the supervised segmentation of macromolecules of interest in subtomograms. The tests of our models on realistically simulated CECT data demonstrate that our new approach has significantly improved segmentation performance compared to our baseline approach. Also, we demonstrate that the proposed model has generalization ability to segment new structures that do not exist in training data. 9 pages |
| Databáze: | OpenAIRE |
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