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Han Y, Zhang Z, Li Y, Fan G, Liang M, Liu Z, Nie S, Ning K, Luo Q, Yuan J. FastCellpose: A Fast and Accurate Deep-Learning Framework for Segmentation of All Glomeruli in Mouse Whole-Kidney Microscopic Optical Images. Cells 2023; 12:2753. [PMID: 38067181 PMCID: PMC10706842 DOI: 10.3390/cells12232753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical imaging techniques allow for the acquisition of mouse whole-kidney 3D datasets at a high resolution. However, fast and accurate analysis of massive imaging data remains a challenge. Here, we propose a deep learning-based segmentation method called FastCellpose to efficiently segment all glomeruli in whole mouse kidneys. Our framework is based on Cellpose, with comprehensive optimization in network architecture and the mask reconstruction process. By means of visual and quantitative analysis, we demonstrate that FastCellpose can achieve superior segmentation performance compared to other state-of-the-art cellular segmentation methods, and the processing speed was 12-fold higher than before. Based on this high-performance framework, we quantitatively analyzed the development changes of mouse glomeruli from birth to maturity, which is promising in terms of providing new insights for research on kidney development and function.
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Affiliation(s)
- Yutong Han
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.H.); (Z.Z.); (Y.L.); (G.F.); (M.L.); (S.N.); (K.N.); (Q.L.)
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Innovation Institute, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou 215123, China
| | - Zhan Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.H.); (Z.Z.); (Y.L.); (G.F.); (M.L.); (S.N.); (K.N.); (Q.L.)
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Innovation Institute, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yafeng Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.H.); (Z.Z.); (Y.L.); (G.F.); (M.L.); (S.N.); (K.N.); (Q.L.)
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Innovation Institute, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou 215123, China
| | - Guoqing Fan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.H.); (Z.Z.); (Y.L.); (G.F.); (M.L.); (S.N.); (K.N.); (Q.L.)
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Innovation Institute, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou 215123, China
| | - Mengfei Liang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.H.); (Z.Z.); (Y.L.); (G.F.); (M.L.); (S.N.); (K.N.); (Q.L.)
| | - Zhijie Liu
- School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China;
| | - Shuo Nie
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.H.); (Z.Z.); (Y.L.); (G.F.); (M.L.); (S.N.); (K.N.); (Q.L.)
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Innovation Institute, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Kefu Ning
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.H.); (Z.Z.); (Y.L.); (G.F.); (M.L.); (S.N.); (K.N.); (Q.L.)
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Innovation Institute, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou 215123, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.H.); (Z.Z.); (Y.L.); (G.F.); (M.L.); (S.N.); (K.N.); (Q.L.)
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Innovation Institute, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Biomedical Engineering, Hainan University, Haikou 570228, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; (Y.H.); (Z.Z.); (Y.L.); (G.F.); (M.L.); (S.N.); (K.N.); (Q.L.)
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Innovation Institute, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou 215123, China
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Li K, Liu B, Wang Z, Li Y, Li H, Wu S, Li Z. Quantitative characterization of zebrafish development based on multiple classifications using Mueller matrix OCT. BIOMEDICAL OPTICS EXPRESS 2023; 14:2889-2904. [PMID: 37342688 PMCID: PMC10278635 DOI: 10.1364/boe.488614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 06/23/2023]
Abstract
Organ development analysis plays an important role in assessing an individual' s growth health. In this study, we present a non-invasive method for the quantitative characterization of zebrafish multiple organs during their growth, utilizing Mueller matrix optical coherence tomography (Mueller matrix OCT) in combination with deep learning. Firstly, Mueller matrix OCT was employed to acquire 3D images of zebrafish during development. Subsequently, a deep learning based U-Net network was applied to segment various anatomical structures, including the body, eyes, spine, yolk sac, and swim bladder of the zebrafish. Following segmentation, the volume of each organ was calculated. Finally, the development and proportional trends of zebrafish embryos and organs from day 1 to day 19 were quantitatively analyzed. The obtained quantitative results revealed that the volume development of the fish body and individual organs exhibited a steady growth trend. Additionally, smaller organs, such as the spine and swim bladder, were successfully quantified during the growth process. Our findings demonstrate that the combination of Mueller matrix OCT and deep learning effectively quantify the development of various organs throughout zebrafish embryonic development. This approach offers a more intuitive and efficient monitoring method for clinical medicine and developmental biology studies.
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Affiliation(s)
- Ke Li
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Bin Liu
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Zaifan Wang
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Yao Li
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Hui Li
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Shulian Wu
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
| | - Zhifang Li
- Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, Fujian, 350007, China
- Bionovel Lab, Guangzhou, Guangdong, 510407, China
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