Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images
This addresses the challenge of nuclei segmentation for tissue analysis in biomedical imaging, but it is incremental as it builds on existing CNN and GAN approaches.
The paper tackles the problem of 3D nuclei instance segmentation in fluorescence microscopy images by proposing a two-stage CNN method that uses synthetic data generation via a spatially constrained cycle-consistent adversarial network to overcome the lack of manual labels, and it outperforms other techniques on multiple real datasets.
Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue. An important step in the characterization of tissue involves nuclei segmentation. In this paper, a two-stage method for segmentation of nuclei using convolutional neural networks (CNNs) is described. In particular, since creating labeled volumes manually for training purposes is not practical due to the size and complexity of the 3D data sets, the paper describes a method for generating synthetic microscopy volumes based on a spatially constrained cycle-consistent adversarial network. The proposed method is tested on multiple real microscopy data sets and outperforms other commonly used segmentation techniques.