NuSEA: Nuclei Segmentation With Ellipse Annotations

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 10; pp. 5996 - 6007
• Main Authors: Meng, Zhu, Dong, Junhao, Zhang, Binyu, Li, Shichao, Wu, Ruixiao, Su, Fei, Wang, Guangxi, Guo, Limei, Zhao, Zhicheng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2024
• Subjects: Accuracy; Annotation tool; Annotations; Deep learning; ellipse annotation; elliptical field; Image segmentation; Manuals; nuclei segmentation; Supervised learning; texture constraint; Training data
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3418106

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.