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Found 6 papers, 2 papers with code
RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid Detection in Three-Dimensional Fluorescence Microscopy Images
Robust and accurate nuclei centroid detection is important for the understanding of biological structures in fluorescence microscopy images.
Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)
By integrating image denoising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained.
Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images
Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue.
Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks
Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy.
Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction
The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods.
Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation
Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue.
