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Liu J, Gao F, Zhang L, Yang H. A Saturation Artifacts Inpainting Method Based on Two-Stage GAN for Fluorescence Microscope Images. MICROMACHINES 2024; 15:928. [PMID: 39064439 PMCID: PMC11279111 DOI: 10.3390/mi15070928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Revised: 07/10/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024]
Abstract
Fluorescence microscopic images of cells contain a large number of morphological features that are used as an unbiased source of quantitative information about cell status, through which researchers can extract quantitative information about cells and study the biological phenomena of cells through statistical and analytical analysis. As an important research object of phenotypic analysis, images have a great influence on the research results. Saturation artifacts present in the image result in a loss of grayscale information that does not reveal the true value of fluorescence intensity. From the perspective of data post-processing, we propose a two-stage cell image recovery model based on a generative adversarial network to solve the problem of phenotypic feature loss caused by saturation artifacts. The model is capable of restoring large areas of missing phenotypic features. In the experiment, we adopt the strategy of progressive restoration to improve the robustness of the training effect and add the contextual attention structure to enhance the stability of the restoration effect. We hope to use deep learning methods to mitigate the effects of saturation artifacts to reveal how chemical, genetic, and environmental factors affect cell state, providing an effective tool for studying the field of biological variability and improving image quality in analysis.
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Affiliation(s)
- Jihong Liu
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (F.G.); (L.Z.)
| | - Fei Gao
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (F.G.); (L.Z.)
| | - Lvheng Zhang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (F.G.); (L.Z.)
| | - Haixu Yang
- Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China;
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Ren J, Che J, Gong P, Wang X, Li X, Li A, Xiao C. Cross comparison representation learning for semi-supervised segmentation of cellular nuclei in immunofluorescence staining. Comput Biol Med 2024; 171:108102. [PMID: 38350398 DOI: 10.1016/j.compbiomed.2024.108102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 01/29/2024] [Accepted: 02/04/2024] [Indexed: 02/15/2024]
Abstract
The morphological analysis of cells from optical images is vital for interpreting brain function in disease states. Extracting comprehensive cell morphology from intricate backgrounds, common in neural and some medical images, poses a significant challenge. Due to the huge workload of manual recognition, automated neuron cell segmentation using deep learning algorithms with labeled data is integral to neural image analysis tools. To combat the high cost of acquiring labeled data, we propose a novel semi-supervised cell segmentation algorithm for immunofluorescence-stained cell image datasets (ISC), utilizing a mean-teacher semi-supervised learning framework. We include a "cross comparison representation learning block" to enhance the teacher-student model comparison on high-dimensional channels, thereby improving feature compactness and separability, which results in the extraction of higher-dimensional features from unlabeled data. We also suggest a new network, the Multi Pooling Layer Attention Dense Network (MPAD-Net), serving as the backbone of the student model to augment segmentation accuracy. Evaluations on the immunofluorescence staining datasets and the public CRAG dataset illustrate our method surpasses other top semi-supervised learning methods, achieving average Jaccard, Dice and Normalized Surface Dice (NSD) indicators of 83.22%, 90.95% and 81.90% with only 20% labeled data. The datasets and code are available on the website at https://github.com/Brainsmatics/CCRL.
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Affiliation(s)
- Jianran Ren
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Jingyi Che
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Peicong Gong
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Xiaojun Wang
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Xiangning Li
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China
| | - Anan Li
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China; Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Chi Xiao
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China; Key Laboratory of Biomedical Engineering of Hainan Province, One Health Institute, Hainan University, Sanya 572025, China.
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Chen R, Liu M, Chen W, Wang Y, Meijering E. Deep learning in mesoscale brain image analysis: A review. Comput Biol Med 2023; 167:107617. [PMID: 37918261 DOI: 10.1016/j.compbiomed.2023.107617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/06/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
Mesoscale microscopy images of the brain contain a wealth of information which can help us understand the working mechanisms of the brain. However, it is a challenging task to process and analyze these data because of the large size of the images, their high noise levels, the complex morphology of the brain from the cellular to the regional and anatomical levels, the inhomogeneous distribution of fluorescent labels in the cells and tissues, and imaging artifacts. Due to their impressive ability to extract relevant information from images, deep learning algorithms are widely applied to microscopy images of the brain to address these challenges and they perform superiorly in a wide range of microscopy image processing and analysis tasks. This article reviews the applications of deep learning algorithms in brain mesoscale microscopy image processing and analysis, including image synthesis, image segmentation, object detection, and neuron reconstruction and analysis. We also discuss the difficulties of each task and possible directions for further research.
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Affiliation(s)
- Runze Chen
- College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China
| | - Min Liu
- College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China; Research Institute of Hunan University in Chongqing, Chongqing, 401135, China.
| | - Weixun Chen
- College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China
| | - Yaonan Wang
- College of Electrical and Information Engineering, National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha, 410082, China
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Sydney 2052, New South Wales, Australia
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Li Z, Shang Z, Liu J, Zhen H, Zhu E, Zhong S, Sturgess RN, Zhou Y, Hu X, Zhao X, Wu Y, Li P, Lin R, Ren J. D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry. Nat Methods 2023; 20:1593-1604. [PMID: 37770711 PMCID: PMC10555838 DOI: 10.1038/s41592-023-01998-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 08/02/2023] [Indexed: 09/30/2023]
Abstract
Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested.
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Affiliation(s)
- Zhongyu Li
- Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Zengyi Shang
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jingyi Liu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Haotian Zhen
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Entao Zhu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Shilin Zhong
- National Institute of Biological Sciences (NIBS), Beijing, China
| | - Robyn N Sturgess
- Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Yitian Zhou
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xuemeng Hu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xingyue Zhao
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Yi Wu
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Peiqi Li
- School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Rui Lin
- National Institute of Biological Sciences (NIBS), Beijing, China
| | - Jing Ren
- Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, UK.
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