LIVECell—A large-scale dataset for label-free live cell segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

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Microsoft COCO: Common Objects in Context

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PyTorch: An Imperative Style, High-Performance Deep Learning Library

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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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FCOS: Fully Convolutional One-Stage Object Detection

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CellProfiler: image analysis software for identifying and quantifying cell phenotypes

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Cellpose: a generalist algorithm for cellular segmentation

Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021) · Nature Methods · doi:10.1038/s41592-020-01018-x · Cited by 4,369 · see citing works

The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalePreprint

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Cascade R-CNN: High Quality Object Detection and Instance Segmentation

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ResNeSt: Split-Attention Networks

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CellProfiler 3.0: Next-generation image processing for biology

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MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ

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Deep learning for cellular image analysis

Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225 · see citing works

Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl

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Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation

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CenterMask: Real-Time Anchor-Free Instance Segmentation

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Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Van Valen, D. A., Kudo, T., Lane, K. M., Macklin, D. N., Quach, N. T., DeFelice, M. M., Maayan, I., Tanouchi, Y., Ashley, E. A., & Covert, M. W. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005177 · Cited by 557 · see citing works

Is Green Fluorescent Protein Toxic to the Living Cells?

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Cell damage and reactive oxygen species production induced by fluorescence microscopy: effect on mitosis and guidelines for non‐invasive fluorescence microscopy

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Animal models and therapeutic molecular targets of cancer: utility and limitations

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Precise Detection in Densely Packed Scenes

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Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning

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The Dark Side of EGFP: Defective Polyubiquitination

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Characterising live cell behaviour: Traditional label-free and quantitative phase imaging approaches

Kasprowicz, R., Suman, R., & O’Toole, P. J. (2017) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2017.01.004 · Cited by 124 · see citing works

Attentive neural cell instance segmentation

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GFP expression in muscle cells impairs actin-myosin interactions: implications for cell therapy

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EVICAN—a balanced dataset for algorithm development in cell and nucleus segmentation

Schwendy, M., Unger, R. E., & Parekh, S. H. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa225 · Cited by 65 · see citing works

Generalized Subset Designs in Analytical Chemistry

Surowiec, I., Vikström, L., Hector, G., Johansson, E., Vikström, C., & Trygg, J. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b00506 · Cited by 41 · see citing works

Induced pluripotent stem cells for disease modeling, cell therapy and drug discovery in genetic autonomic disorders: a review

Saito‐Diaz, K., & Zeltner, N. (2019) · Clinical Autonomic Research · doi:10.1007/s10286-018-00587-4 · Cited by 18 · see citing works

Cellpose 2.0: how to train your own model

Pachitariu, M., & Stringer, C. (2022) · Nature Methods · doi:10.1038/s41592-022-01663-4 · Cited by 1,381 · see citing works

Cellpose3: one-click image restoration for improved cellular segmentation

Stringer, C., & Pachitariu, M. (2025) · Nature Methods · doi:10.1038/s41592-025-02595-5 · Cited by 479 · see citing works

Segment Anything for Microscopy

Archit, A., Freckmann, L., Nair, S., Khalid, N., Hilt, P., Rajashekar, V., Freitag, M., Teuber, C., Spitzner, M., Contreras, C. T., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02580-4 · Cited by 354 · see citing works

Cellpose-SAM: superhuman generalization for cellular segmentationPreprint

Pachitariu, M., Rariden, M., & Stringer, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.28.651001 · Cited by 259 · see citing works

A guide to artificial intelligence for cancer researchers

Pérez-López, R., Laleh, N. G., Mahmood, F., & Kather, J. N. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00694-7 · Cited by 220 · see citing works

The multimodality cell segmentation challenge: toward universal solutions

Ma, J., Xie, R., Ayyadhury, S., Ge, C., Gupta, A., Gupta, R., Gu, S., Zhang, Y., Lee, G., Kim, J., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02233-6 · Cited by 154 · see citing works

Bridging live-cell imaging and next-generation cancer treatment

Alieva, M., Wezenaar, A. K. L., Wehrens, E. J., & Rios, A. C. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00610-5 · Cited by 83 · see citing works

Nuclei segmentation using attention aware and adversarial networks

Göçeri, E. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127445 · Cited by 80 · see citing works

Nucleus segmentation: towards automated solutions

Hollandi, R., Moshkov, N., Paavolainen, L., Tasnádi, E., Piccinini, F., & Horváth, P. (2022) · Trends in Cell Biology · doi:10.1016/j.tcb.2021.12.004 · Cited by 77 · see citing works

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation

Vanrobaeys, Y. S., Peterson, Z. J., Walsh, E. N., Chatterjee, S., Lin, L., Lyons, L. C., Nickl‐Jockschat, T., & Abel, T. G. (2023) · Nature Communications · doi:10.1038/s41467-023-42751-z · Cited by 67 · see citing works

CellSAM: A Foundation Model for Cell SegmentationPreprint

Israel, U., Marks, M., Dilip, R., Li, Q., Yu, C., Laubscher, E., Iqbal, A., Pradhan, E., Ates, A., Abt, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.17.567630 · Cited by 64 · see citing works

Towards foundation models of biological image segmentation

Ma, J., & Wang, B. (2023) · Nature Methods · doi:10.1038/s41592-023-01885-0 · Cited by 57 · see citing works

Ultrack: pushing the limits of cell tracking across biological scales

Bragantini, J., Theodoro, I., Zhao, X., Huijben, T. A. P. M., Hirata-Miyasaki, E., VijayKumar, S., Balasubramanian, A., Lao, T., Agrawal, R., Xiao, S., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02778-0 · Cited by 54 · see citing works

Artificial intelligence for high content imaging in drug discovery

Puigvert, J. C., & Spjuth, O. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102842 · Cited by 51 · see citing works

Segmentation metric misinterpretations in bioimage analysis

Hirling, D., Tasnádi, E., Caicedo, J. C., Caroprese, M., Sjögren, R., Aubreville, M., Koós, K., & Horváth, P. (2023) · Nature Methods · doi:10.1038/s41592-023-01942-8 · Cited by 46 · see citing works

CellSAM: a foundation model for cell segmentation

Marks, M., Israel, U., Dilip, R., Li, Q., Yu, C., Laubscher, E., Iqbal, A., Pradhan, E., Ates, A., Abt, M., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02879-w · Cited by 116 · see citing works

Efficient end-to-end learning for cell segmentation with machine generated weak annotations

Shrestha, P., Kuang, N., & Yu, J. (2023) · Communications Biology · doi:10.1038/s42003-023-04608-5 · Cited by 46 · see citing works

CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models

Metlek, S. (2024) · Neural Computing and Applications · doi:10.1007/s00521-023-09374-3 · Cited by 44 · see citing works

Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis

Kaza, N., Ojaghi, A., & Robles, F. E. (2022) · BME Frontiers · doi:10.34133/2022/9853606 · Cited by 39 · see citing works

Practical guidelines for cell segmentation models under optical aberrations in microscopy

Peng, B., Chen, J., Githinji, P. B., Gul, I., Ye, Q., Chen, M., Qin, P., Huang, X., Yan, C. C., Yu, D., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.09.002 · Cited by 37 · see citing works

Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence

Kusumoto, D., Yuasa, S., & Fukuda, K. (2022) · Pharmaceuticals · doi:10.3390/ph15050562 · Cited by 36 · see citing works

OPLS-Based Multiclass Classification and Data-Driven Interclass Relationship Discovery

Forsgren, E., Björkblom, B., Trygg, J., & Jonsson, P. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01799 · Cited by 34 · see citing works

Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing predictionPreprint

Chen, K., Zhou, Y., Ding, M., Wang, Y., Ren, Z., & Yang, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.31.526427 · Cited by 35 · see citing works

A systematic evaluation of computational methods for cell segmentation

Wang, Y., Zhao, J., Xu, H., Han, C., Tao, Z., Zhou, D., Geng, T., Liu, D., & Ji, Z. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae407 · Cited by 30 · see citing works

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

Schroeder, A. R., Loth, M., Luo, C., Yao, S., Yan, H., Zhang, D., Piya, S., Plowey, E. D., Hu, W., Clemenceau, J. R. V., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02770-8 · Cited by 36 · see citing works

Spatial multiomics of acute myocardial infarction reveals immune cell infiltration through the endocardium

Wünnemann, F., Sicklinger, F., Bestak, K., Nimo, J., Thiemann, T., Amrute, J., Nordbeck, M., Hartmann, N., Ibarra-Arellano, M. A., Tanevski, J., et al. (2025) · Nature Cardiovascular Research · doi:10.1038/s44161-025-00717-y · Cited by 35 · see citing works

Optofluidic imaging meets deep learning: from merging to emerging

Siu, D. M. D., Lee, K. C. M., Chung, B. M. F., Wong, J. S. J., Zheng, G., & Tsia, K. K. (2023) · Lab on a Chip · doi:10.1039/d2lc00813k · Cited by 30 · see citing works

Deep Learning in Single-cell Analysis

Molho, D., Ding, J., Tang, W., Li, Z., Wen, H., Wang, Y., Venegas, J., Jin, W., Liu, R., Su, R., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641284 · Cited by 32 · see citing works

Label-free tumor cells classification using deep learning and high-content imaging

Piansaddhayanon, C., Koracharkornradt, C., Laosaengpha, N., Tao, Q., Ingrungruanglert, P., Israsena, N., Chuangsuwanich, E., & Sriswasdi, S. (2023) · Scientific Data · doi:10.1038/s41597-023-02482-8 · Cited by 28 · see citing works

Deep Learning in Cell Image Analysis

Xu, J., Zhou, D., Deng, D., Li, J., Chen, C., Liao, X., Chen, G., & Heng, P. (2022) · Intelligent Computing · doi:10.34133/2022/9861263 · Cited by 27 · see citing works

Enhanced cell segmentation with limited training datasets using cycle generative adversarial networks

Zargari, A., Topacio, B. R., Mashhadi, N., & Shariati, S. A. (2024) · iScience · doi:10.1016/j.isci.2024.109740 · Cited by 26 · see citing works

Spatial single-cell omics: new insights into liver diseases

Suo, Y., Thimme, R., & Bengsch, B. (2025) · Gut · doi:10.1136/gutjnl-2024-332105 · Cited by 27 · see citing works

Self-supervised machine learning for live cell imagery segmentation

Robitaille, M. C., Byers, J. M., Christodoulides, J. A., & Raphael, M. P. (2022) · Communications Biology · doi:10.1038/s42003-022-04117-x · Cited by 24 · see citing works

A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids

Sockell, A., Wong, W. H., Longwell, S. A., Vu, T., Karlsson, K., Mokhtari, D. A., Schaepe, J. M., Lo, Y., Cornelius, V., Kuo, C. J., et al. (2023) · Cell Systems · doi:10.1016/j.cels.2023.08.002 · Cited by 24 · see citing works

Artificial Intelligence for Cell Segmentation, Event Detection, and Tracking for Label-Free Microscopy Imaging

Maddalena, L., Antonelli, L., Albu, A., Hada, A., & Guarracino, M. R. (2022) · Algorithms · doi:10.3390/a15090313 · Cited by 21 · see citing works

MIA is an open-source standalone deep learning application for microscopic image analysis

Körber, N. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100517 · Cited by 21 · see citing works

Mantis: High-throughput 4D imaging and analysis of the molecular and physical architecture of cells

Ivanov, I. E., Hirata-Miyasaki, E., Chandler, T., Cheloor-Kovilakam, R., Liu, Z., Pradeep, S., Liu, C., Bhave, M., Khadka, S., Arias, C., et al. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae323 · Cited by 21 · see citing works

Label-free live cell recognition and tracking for biological discoveries and translational applications

Chen, B., Yin, Z., Ng, W., Wang, D. M., Tuan, R. S., Bise, R., & Ker, D. F. E. (2024) · npj Imaging · doi:10.1038/s44303-024-00046-y · Cited by 20 · see citing works

Automating Ground Truth Annotations for Gland Segmentation Through Immunohistochemistry

Kataria, T., Rajamani, S., Ayubi, A. B., Bronner, M. P., Jedrzkiewicz, J., Knudsen, B. S., & Elhabian, S. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100331 · Cited by 20 · see citing works

Integrating Artificial Intelligence to Biomedical Science: New Applications for Innovative Stem Cell Research and Drug Development

Kim, M., & Hong, S. (2024) · Technologies · doi:10.3390/technologies12070095 · Cited by 22 · see citing works

S3-VAE: A novel Supervised-Source-Separation Variational AutoEncoder algorithm to discriminate tumor cell lines in time-lapse microscopy images

Casti, P., Cardarelli, S., Comes, M. C., D’Orazio, M., Filippi, J., Antonelli, G., Mencattini, A., Natale, C. D., & Martinelli, E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120861 · Cited by 18 · see citing works

SOAPy: a Python package to dissect spatial architecture, dynamics, and communication

Wang, H., Li, J., Jing, S., Lin, P., Qiu, Y., Yan, X., Yuan, J., Tang, Z., Li, Y., Zhang, H., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03550-5 · Cited by 19 · see citing works

A survey of deep learning methods on cell instance segmentation

Wang, C., Lee, W.-T., & Su, T.-S. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11119-3 · Cited by 17 · see citing works

Transforming microfluidics for single-cell analysis with robotics and artificial intelligence

Cheng, J., Anne, R., & Chen, Y. (2025) · Lab on a Chip · doi:10.1039/d5lc00216h · Cited by 18 · see citing works

Defining the boundaries: challenges and advances in identifying cells in microscopy images

Gogoberidze, N., & Cimini, B. A. (2023) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2023.103055 · Cited by 17 · see citing works

SortedAP: Rethinking evaluation metrics for instance segmentation

Chen, L., Wu, Y., Stegmaier, J., & Merhof, D. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00424 · Cited by 16 · see citing works

In-line image analysis of particulate processes with deep learning: Optimizing training data generation via copy-paste augmentation

Daus, S., Buchwald, T., & Peuker, U. A. (2024) · Powder Technology · doi:10.1016/j.powtec.2024.119884 · Cited by 16 · see citing works

D-MAINS: A Deep-Learning Model for the Label-Free Detection of Mitosis, Apoptosis, Interphase, Necrosis, and Senescence in Cancer Cells

He, S., Sillah, M., Cole, A. R., Uboveja, A., Aird, K. M., Chen, Y., & Gong, Y. (2024) · Cells · doi:10.3390/cells13121004 · Cited by 16 · see citing works

Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis

Mencattini, A., D’Orazio, M., Casti, P., Comes, M. C., Giuseppe, D. D., Antonelli, G., Filippi, J., Corsi, F., Ghibelli, L., Veith, I., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04585-9 · Cited by 15 · see citing works

EPC-DARTS: Efficient partial channel connection for differentiable architecture search

Cai, Z., Chen, L., & Liu, H. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.07.029 · Cited by 15 · see citing works

Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images

Pan, F., Wu, Y., Cui, K., Chen, S., Li, Y., Liu, Y., Shakoor, A., Zhao, H., Lu, B., Zhi, S., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109151 · Cited by 15 · see citing works

Forskolin-Loaded Halloysite Nanotubes as Osteoconductive Additive for the Biopolymer Tissue Engineering Scaffolds

Naumenko, E., Guryanov, I., Zakirova, E. Y., & Fakhrullin, R. F. (2021) · Polymers · doi:10.3390/polym13223949 · Cited by 15 · see citing works

Machine learning-based detection of label-free cancer stem-like cell fate

Chambost, A. J., Berabez, N., Cochet‐Escartin, O., Ducray, F., Gabut, M., Isaac, C., Martel, S., Idbaïh, A., Rousseau, D., Meyronet, D., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-21822-z · Cited by 15 · see citing works

Robust virtual staining of landmark organelles with Cytoland

Liu, Z., Hirata-Miyasaki, E., Pradeep, S., Rahm, J. V., Foley, C., Chandler, T., Ivanov, I. E., Woosley, H. O., Lee, S.-C., Khadka, S., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01046-2 · Cited by 19 · see citing works

Cell Segmentation With Globally Optimized Boundaries (CSGO): A Deep Learning Pipeline for Whole-Cell Segmentation in Hematoxylin-and-Eosin–Stained Tissues

Gu, Z., Wang, S., Rong, R., Zhao, Z., Wu, F., Zhou, Q., Wen, Z., Chi, Z., Fang, Y. V., Peng, Y., et al. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102184 · Cited by 14 · see citing works

Cellular analysis using label-free parallel array microscopy with Fourier ptychography

Wakefield, D. L., Graham, R., Wong, K., Wang, S., Hale, C. M., & Yu, C. (2022) · Biomedical Optics Express · doi:10.1364/boe.451128 · Cited by 13 · see citing works

Generated Pseudo-Labels Guided by Background Skeletons for Overcoming Under-Segmentation in Overlapping Particle Objects

Liu, J., Jiang, Z., Cao, T., Chen, Z., Zhang, C., & Gui, W. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3230451 · Cited by 13 · see citing works

Transformer-based Detection of Microorganisms on High-Resolution Petri Dish Images

Ebert, N., Stricker, D., & Wasenmüller, O. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00428 · Cited by 13 · see citing works

A novel deep learning graph attention network for Alzheimer’s disease image segmentation

Hasan, E., & Wagler, A. E. (2024) · Healthcare Analytics · doi:10.1016/j.health.2024.100310 · Cited by 13 · see citing works

Assessing Large Multimodal Models for One‐Shot Learning and Interpretability in Biomedical Image Classification

Hou, W., Liu, Q., Ma, H., Qu, Y., & Ji, Z. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400947 · Cited by 13 · see citing works

Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation

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Microsnoop: A generalist tool for microscopy image representation

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