UG-Net: Corneal Endothelial Cell Segmentation Based on Uncertainty Estimation and Soft Spatial Attention

Yinglin Zhang1; Wei Wang2; Biao Wang2; Zichao Cai2; Dave Towey1; Ruibin Bai1; Risa Higashita2; Jiang Liu1

10.1109/ISBI53787.2023.10230682

2023

1945-7928

978-1-6654-7359-0

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

2023-April

1-5

18-21 April 2023

Cartagena, Colombia

Corneal endothelial cell segmentation of the microscope image is critical for clinical parameters quantification. However, the low-contrast regions are ambiguous and hard to segment. The uncertainty has been proved to be effective for detecting the ambiguous regions. Besides, spatial attention can guide the model to focus on the region of interest during the training process. This paper proposes an Uncertainty Guided Network (UG-Net) for corneal endothelial cell segmentation based on uncertainty estimation and spatial attention. We first use Bayesian approximation to obtain aleatoric and epistemic uncertainty maps. Then, two uncertainty maps are utilized to guide the model to focus on the low-contrast regions with uncertainty-based soft spatial attention. Experimental results show that the proposed method performs better than other state-of-the-art methods.

Corneal Endothelial Cell Segmentation Uncertainty Estimation Deep Learning

其他

CPCI-S ; EI

WOS:001062050500359

20233914806130

Cytology ; Deep learning ; Image segmentation ; Uncertainty analysis

Biological Materials and Tissue Engineering:461.2 ; Ergonomics and Human Factors Engineering:461.4 ; Biology:461.9 ; Probability Theory:922.1

IEEE

1.School of Computer Science, University of Nottingham Ningbo China, Ningbo, China
2.Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Yinglin Zhang,Wei Wang,Biao Wang,et al. UG-Net: Corneal Endothelial Cell Segmentation Based on Uncertainty Estimation and Soft Spatial Attention[C],2023:1-5.