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Parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289. CAS PubMed Google Scholar Zhou X, Ye Q, Jiang Y, Wang M, Niu Z, Menpes-Smith W, Fang EF, Liu Z, Xia J, Yang G (2020) Systematic and comprehensive automated ventricle segmentation on ventricle images of the elderly patients: a retrospective study. Front Aging Neurosci. PubMed PubMed Central Google Scholar Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation-Nature Methods. Nat Methods 18:203–211. CAS PubMed Google Scholar Haber E, Modersitzki J (2006) Intensity gradient based registration and fusion of multi-modal images. In: Medical image computing and computer-assisted intervention – MICCAI 2006. Berlin, Heidelberg: Springer Berlin Heidelberg. p. 726–733. J, Heldmann S, Kipshagen T, Fischer B (2013) Highly accurate fast lung CT registration. In: SPIE Medical Imaging 2013: Image Processing. Lake Buena Vista, Florida, USA . J, Polzin T, Heldmann S, Simpson IJA, Handels H, Modersitzki J, Heinrich MP (2017) Estimation of large motion in lung CT by integrating regularized keypoint correspondences into dense deformable registration. IEEE Trans Med Imaging 36(8):1746–1757. PubMed Google Scholar Kuckertz S, Papenberg N, Honegger J, Morgas T, Haas B, Heldmann S (2020) Learning deformable image registration with structure guidance constraints for adaptive radiotherapy. In: Biomedical image Registration. Cham: Springer International Publishing. p. 44–53. JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Image Anal 24(1):205–219. PubMed PubMed Central Google Scholar Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 29(15):29. Google Scholar Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recogn 37(1):1–19. Google Scholar Download referencesFundingOpen Access funding enabled and organized by Projekt DEAL.Author informationAuthors and AffiliationsFraunhofer Institute for Digital Medicine MEVIS, Bremen, GermanySina Walluscheck, Luca Canalini, Hannah Strohm, Susanne Diekmann, Jan Klein & Stefan HeldmannAuthorsSina WalluscheckYou can also search for this author inPubMed Google ScholarLuca CanaliniYou can also search for this author inPubMed Google ScholarHannah StrohmYou can also search for this author inPubMed Google ScholarSusanne DiekmannYou can also search for this author inPubMed Google ScholarJan KleinYou can also search for this author inPubMed Google ScholarStefan HeldmannYou can also search for this author inPubMed Google ScholarCorresponding authorCorrespondence to Sina Walluscheck.Ethics declarations Conflict of interest This work was funded by the Federal Ministry of Education and Research of Germany (BMBF) as part of AutoRAD (project number 13GW0491B). Our work

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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898 (2014) Google Scholar Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010) Google Scholar Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. In: Advances in Neural Information Processing Systems, pp. 809–817 (2013) Google Scholar Wang, Y., Liu, J., Chen, Y., Gruteser, M., Yang, J., Liu, H.: E-eyes: device-free location-oriented activity identification using fine-grained wifi signatures. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking (ACM MobiCom), pp. 617–628 (2014) Google Scholar Wang, W., Liu, A.X., Shahzad, M., Ling, K., Lu, S.: Understanding and modeling of wifi signal based human activity recognition. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (ACM MobiCom), pp. 65–76 (2015) Google Scholar Wu, J., Osuntogun, A., Choudhury, T., Philipose, M., Rehg, J.M.: A scalable approach to activity recognition based on object use. In: 11th International Conference on Computer Vision (IEEE ICCV), pp. 1–8 (2007) Google Scholar Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances in Neural Information Processing Systems, pp. 341–349 (2012) Google Scholar Download references

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AbstractThis chapter is an extension of the original publication by Schraivogel et al. (Science 375:315–320, 2022) which described, for the first time, image-enabled and high-speed cell sorting based on the BD CellView technology. It summarizes the technical aspects of the instrument in an easy-to-digest form and provides example-based guidance toward implementation of the CellView-based image cell sorting technology. As an example, it explains how to use the image-enabled cell sorter to analyze the chemically induced fragmentation of the Golgi apparatus in HeLa cells—an experiment that was alluded to in the original publication but was not included in the manuscript due to space constraints. The chemically induced Golgi fragmentation sort illustrates an elegant example of the utility of image-enabled cell sorting as a significant expansion of the single-cell toolbox. It is such a striking phenotype when analyzed with image cytometry but undetectable when using conventional flow cytometry. Described in a straightforward and concise manner, this experiment serves as a standard system assurance for image-based cell sorters. Similar content being viewed by others ReferencesHipp JD, Johann DJ, Chen Y, Madabhushi A, Monaco J, Cheng J et al (2018) Computer-aided laser dissection: a microdissection workflow leveraging image analysis tools. J Pathol Inform 9:45. PubMed PubMed Central Google Scholar Lee K, Kim S, Nam S, Doh J, Chung WK (2022) Upgraded user-friendly image-activated microfluidic cell sorter using an optimized and fast deep learning algorithm. Micromachines (Basel) 13:2105. PubMed Google Scholar Schraivogel D, Kuhn TM, Rauscher B, Rodríguez-Martínez M, Paulsen M, Owsley K et al (2022) High-speed fluorescence image-enabled cell sorting. Science 375:315–320. CAS PubMed PubMed Central Google Scholar Diebold ED, Buckley BW, Gossett DR, Jalali B (2013) Digitally synthesized beat frequency multiplexing for sub-millisecond fluorescence microscopy. Nat Photonics 7:806–810. CAS Google Scholar Storrie B, White J, Röttger S, Stelzer EH, Suganuma T, Nilsson T (1998) Recycling of golgi-resident glycosyltransferases through the ER reveals a novel pathway and provides an explanation for nocodazole-induced golgi scattering. J Cell Biol 143:1505–1521. CAS PubMed PubMed Central Google Scholar Filby A, Carpenter AE (2022) A new image for cell sorting. N Engl J Med 386:1755–1758. PubMed PubMed Central Google Scholar Salek M, Li N, Chou H, Saini K, Jovic A, Jacobs KB et al (2023) COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning. Commun Biol 6:971. PubMed PubMed Central Google Scholar Ota S, Horisaki R, Kawamura Y, Ugawa M, Sato I, Hashimoto K et al (2018) Ghost cytometry. Science 360:1246–1251. CAS PubMed Google Scholar Download references Author informationAuthors and AffiliationsNovo Nordisk Foundation Center for Stem Cell Medicine (reNEW), University of Copenhagen, Copenhagen, DenmarkMalte S. PaulsenAuthorsMalte S. PaulsenYou can also search for this author in PubMed Google ScholarCorresponding authorCorrespondence to Malte S. Paulsen . Editor informationEditors and AffiliationsBethesda, MD, USATeresa S. Hawley Washington, DC, USARobert G. Hawley Rights and permissions Copyright information© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature About this protocolCite this protocolPaulsen, M.S. (2024). Image-Enabled Cell Sorting Using the BD CellView Technology. In: Hawley, T.S., Hawley,

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Technol., IEEE, pp. 706–710Qureshi AM, Deriche M (2014) A review on copy-move image forgery detection techniques, multi-conference on systems. Signals & Devices (SSD):11–14Rao Y, Ni J (2016) A deep learning approach to detection of splicing and copy-move forgeries in images. In: 2016 IEEE Int. Work. Inf. Forensics Secur., IEEE, pp. 1–6Redi JA, Taktak W, Dugelay JL (2011) Digital image forensics: a booklet for beginners. Multimed Tools Appl 51:133–162Article Google Scholar Ryu S-J, Kirchner M, Lee M-J, Lee H-K (2013) Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans Inf Forensics Secur 8:1355–1370Article Google Scholar Salloum R, Ren Y, Kuo C-CJ (2018) Image splicing localization using a multi-task fully convolutional network (MFCN). J Vis Commun Image Represent 51:201–209Article Google Scholar Shivakumar BL, Baboo SS (2011) Detection of region duplication forgery in digital images using SURF. Int J Comput Sci Issues 8:199 Google Scholar Tralic D, Zupancic I, Grgic S, M. Grgic (2013) CoMoFoD — new database for copy-move forgery detection. Proceedings ELMAR-2013, pp. 49–54Wang X, Wang H, Niu S, Zhang J (2019) Detection and localization of image forgeries using improved mask regional convolutional neural network. Math Biosci Eng MBE 16:4581–4593Article Google Scholar Wu Y, Abd-Almageed W, Natarajan P (2018) Busternet: Detecting copy-move image forgery with source/target localization. In: Proc. Eur. Conf. Comput. Vis., pp. 168–184Yang J, Ran P, Xiao D, Tan J (2013) Digital image forgery forensics by using undecimated dyadic wavelet transform and Zernike moments. J Comput Inf Syst 9:6399–6408 Google Scholar Zhang J, Ruan Q, Jin Y (2014) Combined SIFT and bi-coherence features to detect image forgery. In: 2014 12th Int. Conf. Signal Process., IEEE, pp. 1859–1863Zhang W, Yang Z, Niu S, Wang J (2016) Detection of copy-move forgery in flat region based on feature enhancement. In: Int. Work. Digit. Watermarking, Springer, pp. 159–171Zhang Y, Goh J, Win LL, Thing VLL (2016) Image Region Forgery Detection: A Deep Learning Approach., SG-CRC. 2016, 1–11Zhao J, Guo J (2013) Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci Int 233:158–166Article Google Scholar Zheng Y, Cao Y, Chang C-H (2019) A

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11209, pp. 432–448. Springer, Cham (2018). Google Scholar Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853 (2015)Xu, Y., Zhang, J., Zhang, Q., Tao, D.: ViTPose: simple vision transformer baselines for human pose estimation. arXiv preprint arXiv:2204.12484 (2022)Xu, Y., ZHANG, Q., Zhang, J., Tao, D.: ViTAE: Vision transformer advanced by exploring intrinsic inductive bias. In: Advances in Neural Information Processing Systems (2021) Google Scholar Yan, H., Li, Z., Li, W., Wang, C., Wu, M., Zhang, C.: ConTNet: why not use convolution and transformer at the same time? arXiv preprint arXiv:2104.13497 (2021)Yang, J., et al.: Focal attention for long-range interactions in vision transformers. In: Advances in Neural Information Processing Systems (2021) Google Scholar Yang, Z., Liu, D., Wang, C., Yang, J., Tao, D.: Modeling image composition for complex scene generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7764–7773 (2022) Google Scholar Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations (2016) Google Scholar Yuan, L., et al.: Tokens-to-token ViT: training vision transformers from scratch on ImageNet. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 558–567 (2021) Google Scholar Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: CutMix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6023–6032 (2019) Google Scholar Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)Zhang, P., et al.: Multi-scale vision Longformer: a new vision transformer for high-resolution image encoding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2998–3008, October 2021 Google Scholar Zhang, Q., Xu, Y., Zhang, J., Tao, D.: VITAEv2: vision transformer advanced by exploring inductive bias for image recognition and beyond. arXiv preprint arXiv:2202.10108 (2022)Zhang, Q., Yang, Y.B.: Rest: An efficient transformer for visual recognition. In: Advances in Neural Information Processing Systems 34 (2021) Google Scholar Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable ConvNets v2: more deformable, better results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9308–9316 (2019) Google Scholar Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. In: International Conference on Learning Representations (2021) Google Scholar Download references. Begin by downloading Image J from the internet (Image J is a free program in the public domain). Download the version of Image J appropriate for your computer/operating system. a.

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Field image quality assessment via the light field coherence. IEEE Trans. Image Process. 29, 7945–7956 (2020)Article Google Scholar X. Min, J. Zhou, G. Zhai, P. Le Callet, X. Yang, X. Guan, A metric for light field reconstruction, compression, and display quality evaluation. IEEE Trans. Image Process. 29, 3790–3804 (2020)Article Google Scholar C. Meng, P. An, X. Huang, C. Yang, D. Liu, Full reference light field image quality evaluation based on angular-spatial characteristic. IEEE Signal Process. Lett. 27, 525–529 (2020)Article Google Scholar W. Zhou, L. Shi, Z. Chen, J. Zhang, Tensor oriented no-reference light field image quality assessment. IEEE Trans. Image Process. 29, 4070–4084 (2020)Article Google Scholar Y. Liu, G. Jiang, Z. Jiang, Z. Pan, M. Yu, Y.-S. Ho, Pseudoreference subaperture images and microlens image-based blind light field image quality measurement. IEEE Trans. Inst. Meas. 70, 1–15 (2021)Article Google Scholar J. Xiang, G. Jiang, M. Yu, Z. Jiang, Y.-S. Ho, No-reference light field image quality assessment using four-dimensional sparse transform. IEEE Transactions on Multimedia (2021)Q. Qu, X. Chen, V. Chung, Z. Chen, Light field image quality assessment with auxiliary learning based on depthwise and anglewise separable convolutions. IEEE Trans. Broadcast. 67(4), 837–850 (2021)Article Google Scholar P. Zhao, X. Chen, V. Chung, H. Li, Delfiqe-a low-complexity deep learning-based light field image quality evaluator. IEEE Trans. Instrum. Meas. 70, 1–11 (2021) Google Scholar Z. Pan, M. Yu, G. Jiang, H. Xu, Y.-S. Ho, Combining tensor slice and singular value for blind light field image quality assessment. IEEE J. Selected Topics Signal Process. 15(3), 672–687 (2021)Article Google Scholar C. Meng, P. An, X. Huang, C. Yang, L. Shen, B. Wang, Objective quality assessment of lenslet light field image based on focus stack. IEEE Trans. Multimed. 24, 3193–3207 (2021)Article Google Scholar H. Huang, H. Zeng, J. Hou, J. Chen, J. Zhu, K.-K. Ma, A spatial. Begin by downloading Image J from the internet (Image J is a free program in the public domain). Download the version of Image J appropriate for your computer/operating system. a.

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