CASIA OpenIR  > 脑图谱与类脑智能实验室  > 微观重建与智能分析
A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images
Jiazheng, Liu1,4,5; Yafeng, Zheng2,6; Limei, Lin4,5; Jingyue, Guo3,4,5; Yanan, Lv4,5; Jingbin, Yuan3,4,5; Hao, Zhai1,4,5; Xi, Chen4,5; Lijun, Shen4,5; LinLin, Li4,5; Shunong, Bai2,6; Hua, Han1,4,5
Source PublicationJournal of Plant Physiology
2024-03
Pages154236
Abstract

Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.

Indexed BySCI
Language英语
Sub direction classification图像视频处理与分析
planning direction of the national heavy laboratory多尺度信息处理
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57345
Collection脑图谱与类脑智能实验室_微观重建与智能分析
Corresponding AuthorLinLin, Li; Shunong, Bai; Hua, Han
Affiliation1.School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
2.College of Life Sciences, Peking University, Beijing 100871, China
3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
4.Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
5.Team of Microscale Reconstruction and Intelligent Analysis, Laboratory of Brain-AI, Institute of Automation, Chinese Academy of Sciences, Beijing 101499, China
6.State Key Laboratory of Protein and Plant Gene Research, Beijing 100871, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Jiazheng, Liu,Yafeng, Zheng,Limei, Lin,et al. A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images[J]. Journal of Plant Physiology,2024:154236.
APA Jiazheng, Liu.,Yafeng, Zheng.,Limei, Lin.,Jingyue, Guo.,Yanan, Lv.,...&Hua, Han.(2024).A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images.Journal of Plant Physiology,154236.
MLA Jiazheng, Liu,et al."A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images".Journal of Plant Physiology (2024):154236.
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