A dental periapical X-ray images segmentation network based on pixel-wise contrastive learning with dual attention mechanisms

Publication Type

Conference Proceeding Article

Publication Date

7-2025

Abstract

Periapical radiographs tend to have poor quality due to factors like acquisition techniques, equipment limitations, and patient differences. These factors lead to discrepancies in images, making precise segmentation challenging. However, accurate segmentation is crucial in dental practices. To address this issue, deep learning can be employed to improve segmentation accuracy and efficiency, thereby providing more reliable support for clinical diagnosis. In this study, we propose a deep learning network based on an encoder-decoder architecture, which integrates a dual attention mechanism and pixel-wise contrastive learning to address the tooth segmentation problem. We design the Dual Attention Contrast (DAC) module, which enhances feature maps through joint spatial and channel attention before utilizing optimized multi-scale features for both segmentation prediction and pixel-wise contrastive learning. This module, implemented with a multi-level deployment strategy, strengthens the network's ability to extract discriminative features from the multi-scale anatomical structures in periapical radiographs while constructing a globally structured feature space across datasets. The dual attention mechanism improves the recognition of key local features through spatial-channel collaborative calibration, while pixel-wise contrastive learning explicitly constrains the topological structure of the feature space, effectively mitigating the impact of inter-image variations on segmentation accuracy. Experimental results show that the proposed model outperforms current mainstream models across various evaluation metrics in periapical radiograph segmentation tasks.

Discipline

Artificial Intelligence and Robotics | OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2025 International Joint Conference on Neural Networks (IJCNN 2025), Rome, Italy, June 30 - July 5

ISBN

9798331510435

Identifier

10.1109/IJCNN64981.2025.11229102

Publisher

IEEE

City or Country

Pistacataway

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