Treffer: C1M2: a universal algorithm for 3D instance segmentation, annotation, and quantification of irregular cells.
Title:
C1M2: a universal algorithm for 3D instance segmentation, annotation, and quantification of irregular cells.
Authors:
Zheng H; Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China., Huang S; Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.; Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, 570228, China., Zhang J; Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China., Zhang R; Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China., Wang J; Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China., Yuan J; Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China., Li A; Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China., Yang X; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China. xinyang2014@hust.edu.cn., Zhang Z; Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China. czyzzh@mail.hust.edu.cn.; Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, 570228, China. czyzzh@mail.hust.edu.cn.
Source:
Science China. Life sciences [Sci China Life Sci] 2023 Oct; Vol. 66 (10), pp. 2415-2428. Date of Electronic Publication: 2023 May 19.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Science China Press, co-published with Springer Country of Publication: China NLM ID: 101529880 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1869-1889 (Electronic) Linking ISSN: 16747305 NLM ISO Abbreviation: Sci China Life Sci Subsets: MEDLINE
Imprint Name(s):
Original Publication: Beijing : Science China Press, co-published with Springer
MeSH Terms:
References:
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Becheva, Z.R., Gabrovska, K.I., and Godjevargova, T.I. (2018). Comparison between direct and indirect immunofluorescence method for determination of somatic cell count. Chem Pap 72, 1861–1867. (PMID: 10.1007/s11696-018-0445-3)
Belthangady, C., and Royer, L.A. (2019). Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat Methods 16, 1215–1225. (PMID: 10.1038/s41592-019-0458-z31285623)
Blériot, C., Barreby, E., Dunsmore, G., Ballaire, R., Chakarov, S., Ficht, X., De Simone, G., Andreata, F., Fumagalli, V., Guo, W., et al. (2021). A subset of Kupffer cells regulates metabolism through the expression of CD36. Immunity 54, 2101–2116.e6.
Bradley, D., and Roth, G. (2007). Adaptive thresholding using the integral image. J Graphics Tools 12, 13–21. (PMID: 10.1080/2151237X.2007.10129236)
Broaddus, C., Krull, A., Weigert, M., Schmidt, U., and Myers, G. (2020). Removing Structured Noise with Self-Supervised Blind-Spot Networks. In: Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). Iowa City: IEEE. 159–163.
Buades, A., Coll, B., and Morel, J. (2005). A non-local algorithm for image denoising. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego: IEEE. 60–65.
Collins, D.R., Urbach, J.M., Racenet, Z.J., Arshad, U., Power, K.A., Newman, R.M., Mylvaganam, G.H., Ly, N.L., Lian, X., Rull, A., et al. (2021). Functional impairment of HIV-specific CD8 + T cells precedes aborted spontaneous control of viremia. Immunity 54, 2372–2384.e7. (PMID: 10.1016/j.immuni.2021.08.007344962238516715)
David, B.A., Rezende, R.M., Antunes, M.M., Santos, M.M., Freitas Lopes, M.A., Diniz, A.B., Sousa Pereira, R.V., Marchesi, S.C., Alvarenga, D. M., Nakagaki, B.N., et al. (2016). Combination of mass cytometry and imaging analysis reveals origin, location, and functional repopulation of liver myeloid cells in mice. Gastroenterology 151, 1176–1191. (PMID: 10.1053/j.gastro.2016.08.02427569723)
Dijkstra, E.W. (1959). A note on two problems in connexion with graphs. Numer Math 1, 269–271. (PMID: 10.1007/BF01386390)
Donadon, M., Torzilli, G., Cortese, N., Soldani, C., Di Tommaso, L., Franceschini, B., Carriero, R., Barbagallo, M., Rigamonti, A., Anselmo, A., et al. (2020). Macrophage morphology correlates with single-cell diversity and prognosis in colorectal liver metastasis. J Exp Med 217, e20191847. (PMID: 10.1084/jem.20191847327856537596819)
Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm, A., Deubner, J., Jäckel, Z., Seiwald, K., et al. (2019). U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods 16, 351. (PMID: 10.1038/s41592-019-0356-430804552)
Greenwald, N.F., Miller, G., Moen, E., Kong, A., Kagel, A., Dougherty, T., Fullaway, C.C., McIntosh, B.J., Leow, K.X., Schwartz, M.S., et al. (2022). Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat Biotechnol 40, 555–565. (PMID: 10.1038/s41587-021-01094-034795433)
Heindl, S., Gesierich, B., Benakis, C., Llovera, G., Duering, M., and Liesz, A. (2018). Automated morphological analysis of microglia after stroke. Front Cell Neurosci 12, 106. (PMID: 10.3389/fncel.2018.00106297252905917008)
Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., and Maier-Hein, K.H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18, 203–211. (PMID: 10.1038/s41592-020-01008-z33288961)
Korfhage, N., Muhling, M., Ringshandl, S., Becker, A., Schmeck, B., and Freisleben, B. (2020). Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion. Plos Comput Biol 16.
Krull, A., Buchholz, T., and Jug, F. (2019). Noise2Void-Learning Denoising From Single Noisy Images. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE. 2124–2132.
Kunz, L., and Coutu, D.L. (2021). Multicolor 3D confocal imaging of thick tissue sections. Methods Mol Biol 2350, 95–104. (PMID: 10.1007/978-1-0716-1593-5_734331281)
Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., and Aila, T. (2018). Noise2Noise: Learning Image Restoration without Clean Data. Pr Mach Learn Res 80.
Liu, Y., Yang, M., Deng, Y., Su, G., Enninful, A., Guo, C.C., Tebaldi, T., Zhang, D., Kim, D., Bai, Z., et al. (2020). High-spatial-resolution multiomics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18. (PMID: 10.1016/j.cell.2020.10.026331887767736559)
Liu, Z., Xu, M., Huang, S., Pan, Q., Liu, C., Zeng, F., Fan, Z., Lu, Y., Wang, J., Liu, J., et al. (2022). Mesoscale visualization of three-dimensional microvascular architecture and immunocyte distribution in intact mouse liver lobes. Theranostics 12, 5418–5433. (PMID: 10.7150/thno.71718359108009330531)
Maurer, C.R., Rensheng Qi, C.R., and Raghavan, V. (2003). A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Pattern Anal Machine Intell 25, 265–270. (PMID: 10.1109/TPAMI.2003.1177156)
Meyer, F. (1994). Topographic distance and watershed lines. Signal Processing 38, 113–125. (PMID: 10.1016/0165-1684(94)90060-4)
Morales-Navarrete, H., Nonaka, H., Segovia-Miranda, F., Zerial, M., and Kalaidzidis, Y. (2016). Automatic recognition and characterization of different non-parenchymal cells in liver tissue. In: Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging. Prague: IEEE. 536–540.
Morales-Navarrete, H., Segovia-Miranda, F., Klukowski, P., Meyer, K., Nonaka, H., Marsico, G., Chernykh, M., Kalaidzidis, A., Zerial, M., and Kalaidzidis, Y. (2015). A versatile pipeline for the multi-scale digital reconstruction and quantitative analysis of 3D tissue architecture. eLife 4, e11214. (PMID: 10.7554/eLife.11214266738934764584)
Nitta, N., Sugimura, T., Isozaki, A., Mikami, H., Hiraki, K., Sakuma, S., Iino, T., Arai, F., Endo, T., Fujiwaki, Y., et al. (2018). Intelligent image-activated cell sorting. Cell 175, 266–276.e13. (PMID: 10.1016/j.cell.2018.08.02830166209)
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9, 62–66. (PMID: 10.1109/TSMC.1979.4310076)
Ramachandran, P., Matchett, K.P., Dobie, R., Wilson-Kanamori, J.R., and Henderson, N.C. (2020). Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis. Nat Rev Gastroenterol Hepatol 17, 457–472. (PMID: 10.1038/s41575-020-0304-x32483353)
Schraivogel, D., Kuhn, T.M., Rauscher, B., Rodríguez-Martínez, M., Paulsen, M., Owsley, K., Middlebrook, A., Tischer, C., Ramasz, B., Ordoñez-Rueda, D., et al. (2022). High-speed fluorescence image-enabled cell sorting. Science 375, 315–320. (PMID: 10.1126/science.abj3013350506527613231)
Stark, J.A. (2000). Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans Image Process 9, 889–896. (PMID: 10.1109/83.84153418255459)
Stringer, C., Wang, T., Michaelos, M., and Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106. (PMID: 10.1038/s41592-020-01018-x33318659)
van den Boomgaard, R., and van Balen, R. (1992). Methods for fast morphological image transforms using bitmapped binary images. CVGIP-Graphical Model Image Processing 54, 252–258. (PMID: 10.1016/1049-9652(92)90055-3)
Wang, C., Qin, H., Lai, G., Zheng, G., Xiang, H., Wang, J., and Zhang, D. (2020). Automated classification of dual channel dental imaging of auto-fluorescence and white lightby convolutional neural networks. J Innov Opt Health Sci 13, 2050014. (PMID: 10.1142/S1793545820500145)
Zaqout, S., Becker, L.L., and Kaindl, A.M. (2020). Immunofluorescence staining of paraffin sections step by step. Front Neuroanat 14, 582218. (PMID: 10.3389/fnana.2020.582218332400487680859)
Zhang, X., Zhao, W., Zhao, Y., Zhao, Z., Lv, Z., Zhang, Z., Ren, H., Wang, Q., Liu, M., Qian, M., et al. (2022). Inflammatory macrophages exacerbate neutrophil-driven joint damage through ADP/P2Y1 signaling in rheumatoid arthritis. Sci China Life Sci 65, 953–968. (PMID: 10.1007/s11427-020-1957-834480694)
Zhao, Y., Wang, T., Liu, Z., Ke, Y., Li, R., Chen, H., You, Y., Wu, G., Cao, S., Du, Z., et al. (2023). Single-cell transcriptomics of immune cells in lymph nodes reveals their composition and alterations in functional dynamics during the early stages of bubonic plague. Sci China Life Sci 66, 110–126. (PMID: 10.1007/s11427-021-2119-535943690)
Zhong, Q., Li, A., Jin, R., Zhang, D., Li, X., Jia, X., Ding, Z., Luo, P., Zhou, C., Jiang, C., et al. (2021). High-definition imaging using line-illumination modulation microscopy. Nat Methods 18, 309–315. (PMID: 10.1038/s41592-021-01074-x33649587)
Contributed Indexing:
Keywords: 3D instance segmentation; fluorescence images; fluorescence intensity; irregular cells; neural networks; tissue cytometry
Entry Date(s):
Date Created: 20230527 Date Completed: 20231026 Latest Revision: 20231106
Update Code:
20250114
DOI:
10.1007/s11427-022-2327-y
PMID:
37243949
Database:
MEDLINE
Weitere Informationen
Cell instance segmentation is a fundamental task for many biological applications, especially for packed cells in three-dimensional (3D) microscope images that can fully display cellular morphology. Image processing algorithms based on neural networks and feature engineering have enabled great progress in two-dimensional (2D) instance segmentation. However, current methods cannot achieve high segmentation accuracy for irregular cells in 3D images. In this study, we introduce a universal, morphology-based 3D instance segmentation algorithm called Crop Once Merge Twice (C1M2), which can segment cells from a wide range of image types and does not require nucleus images. C1M2 can be extended to quantify the fluorescence intensity of fluorescent proteins and antibodies and automatically annotate their expression levels in individual cells. Our results suggest that C1M2 can serve as a tissue cytometry for 3D histopathological assays by quantifying fluorescence intensity with spatial localization and morphological information.
(© 2023. Science China Press.)