NISNet3D: Three-Dimensional Nuclear Synthesis and Instance Segmentation for Fluorescence Microscopy Images

Author:

Wu LimingORCID,Chen AlainORCID,Salama PaulORCID,Dunn KennethORCID,Delp EdwardORCID

Abstract

AbstractThe primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, researchers generally segment cells by their nuclei. While effective tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task for which few tools have been developed. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present researchers with the opportunity to characterize entire organs. Methods based upon deep-learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper we describe 3D Nuclei Instance Segmentation Network (NISNet3D), a deep learning-based approach in which training is accomplished using synthetic data, profoundly reducing the effort required for network training. We compare results obtained from NISNet3D with results obtained from eight existing techniques.

Publisher

Cold Spring Harbor Laboratory

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Line Is All You Need: Weak Supervision for 2.5D Cell Segmentation;Lecture Notes in Computer Science;2024

2. An Ensemble Method with Edge Awareness for Abnormally Shaped Nuclei Segmentation;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2023-06

3. 3D Ground Truth Annotations of Nuclei in 3D Microscopy Volumes;2022-09-26

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