Image Analysis
| Citation | Link | Keywords |
| Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). | https://www.nature.com/articles/s41592-020-01018-x | Segmentation, Tools, Deep Learning |
| Ershov, D., Phan, MS., Pylvänäinen, J.W. et al. TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Nat Methods 19, 829–832 (2022). | https://www.nature.com/articles/s41592-022-01507-1 | Tracking, Tools |
| Archit, A., Freckmann, L., Nair, S. et al. Segment Anything for Microscopy. Nat Methods 22, 579–591 (2025). | https://www.nature.com/articles/s41592-024-02580-4 | Segmentation, Tools, Deep Learning |
| van Ooijen, Hanna et al.
A thermoplastic chip for 2D and 3D correlative assays combining screening and high-resolution imaging of immune cell responses. Cell Reports Methods, Volume 5, Issue 1, 100965 |
https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(25)00001-3 |
imaging, microwell, correlative imaging, high-resolution, tumor microenvironment, natural killer cell |
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Bray, Mark-Anthony, et al. "Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes." Nature protocols 11.9 (2016): 1757-1774.
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https://www.nature.com/articles/nprot.2016.105 |
segmentation, data analysis, image based biology |
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Gerst, Ruman, Zoltán Cseresnyés, and Marc Thilo Figge. "JIPipe: visual batch processing for ImageJ." nature methods 20.2 (2023): 168-169.
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https://www.nature.com/articles/s41592-022-01744-4 |
bioimage analysis tool |
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Schindelin, Johannes, et al. "Fiji: an open-source platform for biological-image analysis." Nature methods 9.7 (2012): 676-682.
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https://www.nature.com/articles/nmeth.2019 |
bioimage analysis tool |
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Carpenter, Anne E., et al. "CellProfiler: image analysis software for identifying and quantifying cell phenotypes." Genome biology 7.10 (2006): R100.
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https://link.springer.com/article/10.1186/GB-2006-7-10-R100 |
bioimage analysis tool |
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Dosovitskiy, Alexey. "An image is worth 16x16 words: Transformers for image recognition at scale." arXiv preprint arXiv:2010.11929 (2020).
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https://arxiv.org/pdf/2010.11929/1000 |
Deep Learning |
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Ronneberger, O., Fischer, P. and Brox, T., 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Cham: Springer international publishing.
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https://arxiv.org/pdf/1505.04597 |
U-Net, Deep Learning, Biomedical image segmentation |
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Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B. and Glocker, B., 2018. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.
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https://arxiv.org/pdf/1804.03999 |
Attention U-Net |
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Jiang, J., Chen, X., Tian, G. and Liu, Y., 2023, April. ViG-UNet: vision graph neural networks for medical image segmentation. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE.
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Graph neural networks, U-Net
Ardila, D., Kiraly, A.P., Bharadwaj, S., Choi, B., Reicher, J.J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G. and Naidich, D.P., 2019. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature medicine, 25(6), pp.954-961.
Deep Learning (3D), lung cancer detection, computed tomography
McKinney, S.M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G.S., Darzi, A. and Etemadi, M., 2020. International evaluation of an AI system for breast cancer screening. Nature, 577(7788), pp.89-94.
AI system for breast cancer screening
Chen, T., Kornblith, S., Swersky, K., Norouzi, M. and Hinton, G.E., 2020. Big self-supervised models are strong semi-supervised learners. Advances in neural information processing systems, 33, pp.22243-22255.
Big self-supervised models
Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T. and Natarajan, V., 2021. Big self-supervised models advance medical image classification. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 3478-3488).
Big self-supervised models
Zhao, L., Jia, C., Ma, J., Shao, Y., Liu, Z. and Yuan, H., 2023. Medical image segmentation based on self-supervised hybrid fusion network. Frontiers in Oncology, 13, p.1109786.
Self-supervised medical image segmentation