ArticleProceedings. International Conference on Image Processing2018
DEEP LEARNING BASED SUPERVISED SEMANTIC SEGMENTATION OF ELECTRON CRYO-SUBTOMOGRAMS.
Article in Proceedings. International Conference on Image Processing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments.Innovation (Cambridge (Mass.)) · 2024Review
- A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation.BMC bioinformatics · 2020Article
- One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography.Frontiers in molecular biosciences · 2020Article
- Adversarial domain adaptation for cross data source macromolecule in situ structural classification in cellular electron cryo-tomograms.Bioinformatics (Oxford, England) · 2019Article
- Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography.BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference · 2018Article
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
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