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Nuclei counting in microscopy images with three dimensional generative adversarial networks.

, , , , , and . Medical Imaging: Image Processing, volume 10949 of SPIE Proceedings, page 109492Y. SPIE, (2019)

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Segmentation of fluorescence microscopy images using three dimensional active contours with inhomogeneity correction., , , and . ISBI, page 709-713. IEEE, (2017)Three Dimensional Nuclei Segmentation and Classification of Fluorescence Microscopy Images., , , , , , and . ISBI, page 1-5. IEEE, (2020)Boundary fitting based segmentation of fluorescence microscopy images., , , and . IMAWM, volume 9408 of SPIE Proceedings, page 940805. SPIE, (2015)Nuclei counting in microscopy images with three dimensional generative adversarial networks., , , , , and . Medical Imaging: Image Processing, volume 10949 of SPIE Proceedings, page 109492Y. SPIE, (2019)Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation., , , , , , and . CVPR Workshops, page 2221-2229. Computer Vision Foundation / IEEE Computer Society, (2018)Three Dimensional Blind Image Deconvolution for Fluorescence Microscopy using Generative Adversarial Networks., , , , and . ISBI, page 538-542. IEEE, (2019)Fluorescence Microscopy Image Segmentation Using Convolutional Neural Network With Generative Adversarial Networks., , , , , , and . CoRR, (2018)Background subtraction using the factored 3-way restricted Boltzmann machines., and . CoRR, (2018)Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction., , , , and . Computational Imaging, Society for Imaging Science and Technology, (2018)