NuSEA: Nuclei Segmentation With Ellipse Annotations

Objective: Nuclei segmentation is a crucial pre-task for pathological microenvironment quantification. However, the acquisition of manually precise nuclei annotations for improving the performance of deep learning models is time-consuming and expensive. Methods: In this paper, an efficient nuclear a...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2024-10, Vol.28 (10), p.5996-6007
Main Authors: Meng, Zhu, Dong, Junhao, Zhang, Binyu, Li, Shichao, Wu, Ruixiao, Su, Fei, Wang, Guangxi, Guo, Limei, Zhao, Zhicheng
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Annotation tool
Annotations
Cell Nucleus
Databases, Factual
Deep Learning
ellipse annotation
elliptical field
Humans
Image Interpretation, Computer-Assisted - methods
Image Processing, Computer-Assisted - methods
Image segmentation
Manuals
nuclei segmentation
Supervised learning
texture constraint
Training data
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Summary:Objective: Nuclei segmentation is a crucial pre-task for pathological microenvironment quantification. However, the acquisition of manually precise nuclei annotations for improving the performance of deep learning models is time-consuming and expensive. Methods: In this paper, an efficient nuclear annotation tool called NuSEA is proposed to achieve accurate nucleus segmentation, where a simple but effective ellipse annotation is applied. Specifically, the core network U-Light of NuSEA is lightweight with only 0.86 M parameters, which is suitable for real-time nuclei segmentation. In addition, an Elliptical Field Loss and a Texture Loss are proposed to enhance the edge segmentation and constrain the smoothness simultaneously. Results: Extensive experiments on three public datasets (MoNuSeg, CPM-17, and CoNSeP) demonstrate that NuSEA is superior to the state-of-the-art (SOTA) methods and better than existing algorithms based on point, rectangle, and text annotations. Conclusions: With the assistance of NuSEA, a new dataset called NuSEA-dataset v1.0, encompassing 118,857 annotated nuclei from the whole-slide images of 12 organs is released. Significance: NuSEA provides a rapid and effective annotation tool for nuclei in histopathological images, benefiting future explorations in deep learning algorithms.
ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2024.3418106