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Chang S, Li L, Hong B, Liu J, Xu Y, Pang K, Zhang L, Han H, Chen X. An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography. BMC Biol 2023; 21:198. [PMID: 37743470 PMCID: PMC10519085 DOI: 10.1186/s12915-023-01696-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 09/06/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND As an extension of electron tomography (ET), serial section electron tomography (serial section ET) aims to align the tomographic images of multiple thick tissue sections together, to break through the volume limitation of the single section and preserve the sub-nanoscale voxel size. It could be applied to reconstruct the intact synapse, which expands about one micrometer and contains nanoscale vesicles. However, there are several drawbacks of the existing serial section ET methods. First, locating and imaging regions of interest (ROIs) in serial sections during the shooting process is time-consuming. Second, the alignment of ET volumes is difficult due to the missing information caused by section cutting and imaging. Here we report a workflow to simplify the acquisition of ROIs in serial sections, automatically align the volume of serial section ET, and semi-automatically reconstruct the target synaptic structure. RESULTS We propose an intelligent workflow to reconstruct the intact synapse with sub-nanometer voxel size. Our workflow includes rapid localization of ROIs in serial sections, automatic alignment, restoration, assembly of serial ET volumes, and semi-automatic target structure segmentation. For the localization and acquisition of ROIs in serial sections, we use affine transformations to calculate their approximate position based on their relative location in orderly placed sections. For the alignment of consecutive ET volumes with significantly distinct appearances, we use multi-scale image feature matching and the elastic with belief propagation (BP-Elastic) algorithm to align them from coarse to fine. For the restoration of the missing information in ET, we first estimate the number of lost images based on the pixel changes of adjacent volumes after alignment. Then, we present a missing information generation network that is appropriate for small-sample of ET volume using pre-training interpolation network and distillation learning. And we use it to generate the missing information to achieve the whole volume reconstruction. For the reconstruction of synaptic ultrastructures, we use a 3D neural network to obtain them quickly. In summary, our workflow can quickly locate and acquire ROIs in serial sections, automatically align, restore, assemble serial sections, and obtain the complete segmentation result of the target structure with minimal manual manipulation. Multiple intact synapses in wild-type rat were reconstructed at a voxel size of 0.664 nm/voxel to demonstrate the effectiveness of our workflow. CONCLUSIONS Our workflow contributes to obtaining intact synaptic structures at the sub-nanometer scale through serial section ET, which contains rapid ROI locating, automatic alignment, volume reconstruction, and semi-automatic synapse reconstruction. We have open-sourced the relevant code in our workflow, so it is easy to apply it to other labs and obtain complete 3D ultrastructures which size is similar to intact synapses with sub-nanometer voxel size.
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Affiliation(s)
- Sheng Chang
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, 100190, Beijing, China
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Linlin Li
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Bei Hong
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, 100190, Beijing, China
| | - Jing Liu
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Yuxuan Xu
- School of Software and Microelectronics, Peking University, 100871, Beijing, China
| | - Keliang Pang
- School of Pharmaceutical Sciences, Tsinghua University, 100084, Beijing, China
| | - Lina Zhang
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Hua Han
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
| | - Xi Chen
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
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Bafti SM, Ang CS, Hossain MM, Marcelli G, Alemany-Fornes M, Tsaousis AD. A crowdsourcing semi-automatic image segmentation platform for cell biology. Comput Biol Med 2021; 130:104204. [PMID: 33429139 DOI: 10.1016/j.compbiomed.2020.104204] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/24/2020] [Accepted: 12/26/2020] [Indexed: 12/21/2022]
Abstract
State-of-the-art computer-vision algorithms rely on big and accurately annotated data, which are expensive, laborious and time-consuming to generate. This task is even more challenging when it comes to microbiological images, because they require specialized expertise for accurate annotation. Previous studies show that crowdsourcing and assistive-annotation tools are two potential solutions to address this challenge. In this work, we have developed a web-based platform to enable crowdsourcing annotation of image data; the platform is powered by a semi-automated assistive tool to support non-expert annotators to improve the annotation efficiency. The behavior of annotators with and without the assistive tool is analyzed, using biological images of different complexity. More specifically, non-experts have been asked to use the platform to annotate microbiological images of gut parasites, which are compared with annotations by experts. A quantitative evaluation is carried out on the results, confirming that the assistive tools can noticeably decrease the non-expert annotation's cost (time, click, interaction, etc.) while preserving or even improving the annotation's quality. The annotation quality of non-experts has been investigated using IoU (intersection over union), precision and recall; based on this analysis we propose some ideas on how to better design similar crowdsourcing and assistive platforms.
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Affiliation(s)
- Saber Mirzaee Bafti
- School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NZ, UK.
| | - Chee Siang Ang
- School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NZ, UK
| | - Md Moinul Hossain
- School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NZ, UK
| | - Gianluca Marcelli
- School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NZ, UK
| | - Marc Alemany-Fornes
- Laboratory of Molecular & Evolutionary Parasitology, RAPID Group, School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK
| | - Anastasios D Tsaousis
- Laboratory of Molecular & Evolutionary Parasitology, RAPID Group, School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK
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