References

1.
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: A generalist algorithm for cellular segmentation. Nature Methods 18, 100–106 (2021).
2.
Chamier, L. von et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications 12, 2276 (2021).
3.
4.
Wu, Y. & Shroff, H. Multiscale fluorescence imaging of living samples. Histochemistry and Cell Biology 158, 301–323 (2022).
5.
Schermelleh, L., Heintzmann, R. & Leonhardt, H. A guide to super-resolution fluorescence microscopy. Journal of Cell Biology 190, 165–175 (2010).
6.
Archit, A. et al. Segment anything for microscopy. Nature Methods 22, 579–591 (2025).
7.
Sahl, S. J., Hell, S. W. & Jakobs, S. Fluorescence nanoscopy in cell biology. Nature Reviews Molecular Cell Biology 18, 685–701 (2017).
8.
Schermelleh, L. et al. Super-resolution microscopy demystified. Nature Cell Biology 21, 72–84 (2019).
9.
Ji, N. Adaptive optical fluorescence microscopy. Nature Methods 14, 374–380 (2017).
10.
Hampson, K. M. et al. Adaptive optics for high-resolution imaging. Nature Reviews Methods Primers 1, 68 (2021).
11.
Shroff, H., Testa, I., Jug, F. & Manley, S. Live-cell imaging powered by computation. Nature Reviews Molecular Cell Biology 25, 443–463 (2024).
12.
Venkatesh, M., Mohan, K. & Seelamantula, C. S. Directional bilateral filters for smoothing fluorescence microscopy images. AIP Advances 5, 084805 (2015).
13.
Danielyan, A., Wu, Y.-W., Shih, P.-Y., Dembitskaya, Y. & Semyanov, A. Denoising of two-photon fluorescence images with block-matching 3D filtering. Methods 68, 308–316 (2014).
14.
Zhang, Y. et al. A poisson-gaussian denoising dataset with real fluorescence microscopy images. in 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR) 11702–11710 (Optica Publishing Group, 2019). doi:10.1109/CVPR.2019.01198.
15.
Li, J., Luisier, F. & Blu, T. Pure-let deconvolution of 3D fluorescence microscopy images. in 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017) 723–727 (2017). doi:10.1109/ISBI.2017.7950621.
16.
Makitalo, M. & Foi, A. Optimal inversion of the generalized anscombe transformation for poisson-gaussian noise. IEEE Transactions on Image Processing 22, 91–103 (2013).
17.
Luisier, F., Vonesch, C., Blu, T. & Unser, M. Fast interscale wavelet denoising of poisson-corrupted images. Signal Processing 90, 415–427 (2010).
18.
Walt, S. van der et al. Scikit-image: Image processing in python. PeerJ 2, e453 (2014).
19.
Wiener, N. Extrapolation, Interpolation, and Smoothing of Stationary Time Series: With Engineering Applications. (The MIT Press, 1949). doi:10.7551/mitpress/2946.001.0001.
20.
Tikhonov, A. N. Solution of incorrectly formulated problems and the regularization method. Soviet Math. Dokl. 4, 1035–1038 (1963).
21.
Miller, K. Least squares methods for ill-posed problems with a prescribed bound. SIAM Journal on Mathematical Analysis 1, 52–74 (1970).
22.
Beck, A. & Teboulle, M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences 2, 183–202 (2009).
23.
Lucy, L. B. An iterative technique for the rectification of observed distributions. Astronomical Journal 79, 745 (1974).
24.
Richardson, W. H. Bayesian-based iterative method of image restoration*. J. Opt. Soc. Am. 62, 55–59 (1972).
25.
Sarder, P. & Nehorai, A. Deconvolution methods for 3-d fluorescence microscopy images. IEEE Signal Processing Magazine 23, 32–45 (2006).
26.
Goodwin, P. C. Chapter 10 - quantitative deconvolution microscopy. in Quantitative imaging in cell biology (eds. Waters, J. C. & Wittman, T.) vol. 123 177–192 (Academic Press, 2014).
27.
Guo, M. et al. Rapid image deconvolution and multiview fusion for optical microscopy. Nature Biotechnology 38, 1337–1346 (2020).
28.
Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nature Methods 9, 676–682 (2012).
29.
30.
Bazin, P.-L. et al. Volumetric neuroimage analysis extensions for the MIPAV software package. Journal of Neuroscience Methods 165, 111–121 (2007).
31.
Booth, M. J. Adaptive optics in microscopy. Philosophical Transactions: Mathematical, Physical and Engineering Sciences 365, 2829–2843 (2007).
32.
Hell, S. W. Far-field optical nanoscopy. Science 316, 1153–1158 (2007).
33.
Vicidomini, G., Bianchini, P. & Diaspro, A. STED super-resolved microscopy. Nature Methods 15, 173–182 (2018).
34.
Wu, Y. & Shroff, H. Faster, sharper, and deeper: Structured illumination microscopy for biological imaging. Nature Methods 15, 1011–1019 (2018).
35.
Chen, F., Tillberg, P. W. & Boyden, E. S. Expansion microscopy. Science 347, 543–548 (2015).
36.
Wassie, A. T., Zhao, Y. & Boyden, E. S. Expansion microscopy: Principles and uses in biological research. Nature Methods vol. 16 33–41 (2019).
37.
Valli, J. et al. Seeing beyond the limit: A guide to choosing the right super-resolution microscopy technique. Journal of Biological Chemistry 297, 100791 (2021).
38.
39.
Hagen, G. M. et al. Fluorescence microscopy datasets for training deep neural networks. GigaScience 10, giab032 (2021).
40.
Weigert, M. et al. Content-aware image restoration: Pushing the limits of fluorescence microscopy. Nature Methods 15, 1090–1097 (2018).
41.
42.
43.
44.
Zhang, K., Zuo, W., Chen, Y., Meng, D. & Zhang, L. Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing 26, 3142–3155 (2017).
45.
Dabov, K., Foi, A., Katkovnik, V. & Egiazarian, K. Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Transactions on Image Processing 16, 2080–2095 (2007).
46.
Krull, A., Buchholz, T.-O. & Jug, F. Noise2Void - learning denoising from single noisy images. in 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR) 2124–2132 (2019). doi:10.1109/CVPR.2019.00223.
47.
Batson, J. & Royer, L. Noise2Self: Blind denoising by self-supervision. in Proceedings of the 36th international conference on machine learning (eds. Chaudhuri, K. & Salakhutdinov, R.) vol. 97 524–533 (PMLR, 2019).
48.
Lehtinen, J. et al. Noise2Noise: Learning image restoration without clean data. in Proceedings of the 35th international conference on machine learning (eds. Dy, J. & Krause, A.) vol. 80 2965–2974 (PMLR, 2018).
49.
50.
Yanny, K., Monakhova, K., Shuai, R. W. & Waller, L. Deep learning for fast spatially varying deconvolution. Optica 9, 96–99 (2022).
51.
Saha, D. et al. Practical sensorless aberration estimation for 3D microscopy with deep learning. Opt. Express 28, 29044–29053 (2020).
52.
Kang, I., Zhang, Q., Yu, S. X. & Ji, N. Coordinate-based neural representations for computational adaptive optics in widefield microscopy. Nature Machine Intelligence 6, 714–725 (2024).
53.
Kang, I. et al. Adaptive optical correction in in vivo two-photon fluorescence microscopy with neural fields. bioRxiv (2024) doi:10.1101/2024.10.20.619284.
54.
Fersini, F. et al. Wavefront estimation through structured detection in laser scanning microscopy. Biomed. Opt. Express 16, 2135–2155 (2025).
55.
Zhou, Y., Jin, Z., Zhao, Q., Xiong, B. & Cao, X. Aberration modeling in deep learning for volumetric reconstruction of light-field microscopy. Laser & Photonics Reviews 17, 2300154 (2023).
56.
57.
Hu, L., Hu, S., Gong, W. & Si, K. Image enhancement for fluorescence microscopy based on deep learning with prior knowledge of aberration. Opt. Lett. 46, 2055–2058 (2021).
58.
Wang, H. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nature Methods 16, 103–110 (2019).
59.
60.
61.
Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. in Medical image computing and computer-assisted intervention – MICCAI 2015 (eds. Navab, N., Hornegger, J., Wells, W. M. & Frangi, A. F.) 234–241 (Springer International Publishing, Cham, 2015). doi:10.1007/978-3-319-24574-4_28.
62.
Ji, Z., Li, J. D. & Telgarsky, M. Early-stopped neural networks are consistent. in Advances in neural information processing systems 34 - 35th conference on neural information processing systems, NeurIPS 2021 (eds. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, {Percy. S. }. & Vaughan}, J. {Wortman) 1805–1817 (Neural information processing systems foundation, 2021).
63.
Santos, C. F. G. D. & Papa, J. P. Avoiding overfitting: A survey on regularization methods for convolutional neural networks. ACM Comput. Surv. 54, (2022).
64.
Miseta, T., Fodor, A. & Vathy-Fogarassy, Á. Surpassing early stopping: A novel correlation-based stopping criterion for neural networks. Neurocomputing 567, 127028 (2024).
65.
Shah, Z. H. et al. Image restoration in frequency space using complex-valued CNNs. Frontiers in Artificial Intelligence Volume 7 - 2024, (2024).
66.
Liu, J., Gao, F., Zhang, L. & Yang, H. A saturation artifacts inpainting method based on two-stage GAN for fluorescence microscope images. Micromachines 15, (2024).
67.
Bouchard, C. et al. Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition. Nature Machine Intelligence 5, 830–844 (2023).
68.
69.
70.
71.
Osuna-Vargas, P. et al. Denoising diffusion models for high-resolution microscopy image restoration. in 2025 IEEE/CVF winter conference on applications of computer vision (WACV) 4320–4330 (2025). doi:10.1109/WACV61041.2025.00424.
72.
73.
Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 600–612 (2004).
74.
Gonzalez, R. C. & Woods, R. E. Digital Image Processing. (Prentice Hall, 2008).
75.
Haase, R., Tischer, C., Bankhead, P., Miura, K. & Cimini, B. A call for FAIR and open-access training materials to advance BioImage analysis. (2024).
76.
Ljosa, V., Sokolnicki, K. L. & Carpenter, A. E. Annotated high-throughput microscopy image sets for validation. Nat. Methods 9, 637 (2012).
77.
Cimini, B. A. When to say ’good enough’. (2019).
78.
Jamali, N., Dobson, E. T. A., Eliceiri, K. W., Carpenter, A. E. & Cimini, B. A. 2020 BioImage analysis survey: Community experiences and needs for the future. Biological Imaging 1, e4 (2021).
79.
Sivagurunathan, S. et al. Bridging imaging users to imaging analysis - a community survey. J. Microsc. (2023).
80.
Schindelin, J., Rueden, C. T., Hiner, M. C. & Eliceiri, K. W. The ImageJ ecosystem: An open platform for biomedical image analysis. Mol. Reprod. Dev. 82, 518–529 (2015).
81.
Napari: A multi-dimensional image viewer for python. doi:10.5281/zenodo.3555620.
82.
Stirling, D. R. et al. CellProfiler 4: Improvements in speed, utility and usability. BMC Bioinformatics 22, 433 (2021).
83.
Bankhead, P. et al. QuPath: Open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
84.
Chaumont, F. de et al. Icy: An open bioimage informatics platform for extended reproducible research. Nat. Methods 9, 690–696 (2012).
85.
Berg, S. et al. Ilastik: Interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
86.
Arzt, M. et al. LABKIT: Labeling and segmentation toolkit for big image data. Front. Comput. Sci. 4, (2022).
87.
Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell detection with star-convex polygons. in Medical image computing and computer assisted intervention – MICCAI 2018 (eds. Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 265–273 (Springer International Publishing, Cham, 2018). doi:10.1007/978-3-030-00934-2_30.
88.
89.
Pachitariu, M. & Stringer, C. Cellpose 2.0: How to train your own model. Nat. Methods 19, 1634–1641 (2022).
90.
91.
Ouyang, W. et al. BioImage model zoo: A community-driven resource for accessible deep learning in BioImage analysis. bioRxiv 2022.06.07.495102 (2022) doi:10.1101/2022.06.07.495102.
92.
93.
Gómez-de-Mariscal, E. et al. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nat. Methods 18, 1192–1195 (2021).
94.
Ouyang, W., Mueller, F., Hjelmare, M., Lundberg, E. & Zimmer, C. ImJoy: An open-source computational platform for the deep learning era. Nat. Methods 16, 1199–1200 (2019).
95.
Shah, R., Gogoberidze, N. & Cimini, B. Bilayers.
96.
Hidalgo-Cenalmor, I. et al. DL4MicEverywhere: Deep learning for microscopy made flexible, shareable and reproducible. Nat. Methods 21, 925–927 (2024).
97.
Kluyver, T. et al. Jupyter notebooks - a publishing format for reproducible computational workflows. in Positioning and power in academic publishing: Players, agents and agendas (eds. Loizides, F. & Scmidt, B.) 87–90 (IOS Press, Netherlands, 2016).
98.
Kreshuk, A. & Zhang, C. Machine learning: Advanced image segmentation using ilastik. Methods Mol. Biol. 2040, 449–463 (2019).
99.
Arganda-Carreras, I. et al. Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).
100.
Fazeli, E. et al. Automated cell tracking using StarDist and TrackMate. F1000Research 9, 1279 (2020).
101.
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. in Advances in Neural Information Processing Systems vol. 25 (Curran Associates, Inc., 2012).
102.
Ahlers, J. et al. Napari: A multi-dimensional image viewer for Python. (2023) doi:10.5281/zenodo.8115575.
103.
Russell, C. T. et al. Bia-binder: A web-native cloud compute service for the bioimage analysis community. (2024) doi:10.48550/arXiv.2411.12662.
104.
Follain, G. et al. Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers. (2024) doi:10.1101/2024.09.30.615654.
105.
Moen, E. et al. Deep learning for cellular image analysis. Nature Methods 16, 1233–1246 (2019).
106.
Pylvänäinen, J. W., Gómez-de-Mariscal, E., Henriques, R. & Jacquemet, G. Live-cell imaging in the deep learning era. Current Opinion in Cell Biology 85, 102271 (2023).
107.
Laine, R. F., Arganda-Carreras, I., Henriques, R. & Jacquemet, G. Avoiding a replication crisis in deep-learning-based bioimage analysis. Nature methods 18, 1136–1144 (2021).
108.
Heinrich, L. et al. Whole-cell organelle segmentation in volume electron microscopy. Nature 599, 141–146 (2021).
109.
110.
Moshkov, N. et al. Learning representations for image-based profiling of perturbations. Nature Communications 15, 1594 (2024).
111.
Caicedo, J. C., McQuin, C., Goodman, A., Singh, S. & Carpenter, A. E. Weakly Supervised Learning of Single-Cell Feature Embeddings. in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 9309–9318 (2018). doi:10.1109/CVPR.2018.00970.
112.
Li, Y. & Shen, L. cC-GAN: A Robust Transfer-Learning Framework for HEp-2 Specimen Image Segmentation. IEEE Access 6, 14048–14058 (2018).
113.
Morid, M. A., Borjali, A. & Del Fiol, G. A scoping review of transfer learning research on medical image analysis using ImageNet. Computers in Biology and Medicine 128, 104115 (2021).
114.
Kochetov, B. et al. UNSEG: Unsupervised segmentation of cells and their nuclei in complex tissue samples. Communications Biology 7, 1–14 (2024).
115.
Chen, J. et al. The Allen Cell and Structure Segmenter: A new open source toolkit for segmenting 3D intracellular structures in fluorescence microscopy images. (2020) doi:10.1101/491035.
116.
117.
118.
119.
Rangel DaCosta, L., Sytwu, K., Groschner, C. K. & Scott, M. C. A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM). npj Computational Materials 10, 1–11 (2024).
120.
Lin, B. et al. A deep learned nanowire segmentation model using synthetic data augmentation. npj Computational Materials 8, 1–12 (2022).
121.
Shorten, C. & Khoshgoftaar, T. M. A survey on Image Data Augmentation for Deep Learning. Journal of Big Data 6, 60 (2019).
122.
Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 2278–2324 (1998).
123.
Alibrahim, H. & Ludwig, S. A. Hyperparameter optimization: Comparing genetic algorithm against grid search and bayesian optimization. in 2021 IEEE congress on evolutionary computation (CEC) 1551–1559 (2021). doi:10.1109/CEC45853.2021.9504761.
124.
Ilievski, I., Akhtar, T., Feng, J. & Shoemaker, C. Efficient hyperparameter optimization for deep learning algorithms using deterministic RBF surrogates. Proceedings of the AAAI Conference on Artificial Intelligence 31, (2017).