Back to Search Start Over

MARes-Net: multi-scale attention residual network for jaw cyst image segmentation

Authors :
Xiaokang Ding
Xiaoliang Jiang
Huixia Zheng
Hualuo Shi
Ban Wang
Sixian Chan
Source :
Frontiers in Bioengineering and Biotechnology, Vol 12 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

Jaw cyst is a fluid-containing cystic lesion that can occur in any part of the jaw and cause facial swelling, dental lesions, jaw fractures, and other associated issues. Due to the diversity and complexity of jaw images, existing deep-learning methods still have challenges in segmentation. To this end, we propose MARes-Net, an innovative multi-scale attentional residual network architecture. Firstly, the residual connection is used to optimize the encoder-decoder process, which effectively solves the gradient disappearance problem and improves the training efficiency and optimization ability. Secondly, the scale-aware feature extraction module (SFEM) significantly enhances the network’s perceptual abilities by extending its receptive field across various scales, spaces, and channel dimensions. Thirdly, the multi-scale compression excitation module (MCEM) compresses and excites the feature map, and combines it with contextual information to obtain better model performance capabilities. Furthermore, the introduction of the attention gate module marks a significant advancement in refining the feature map output. Finally, rigorous experimentation conducted on the original jaw cyst dataset provided by Quzhou People’s Hospital to verify the validity of MARes-Net architecture. The experimental data showed that precision, recall, IoU and F1-score of MARes-Net reached 93.84%, 93.70%, 86.17%, and 93.21%, respectively. Compared with existing models, our MARes-Net shows its unparalleled capabilities in accurately delineating and localizing anatomical structures in the jaw cyst image segmentation.

Details

Language :
English
ISSN :
22964185
Volume :
12
Database :
Directory of Open Access Journals
Journal :
Frontiers in Bioengineering and Biotechnology
Publication Type :
Academic Journal
Accession number :
edsdoj.596305fc6b914137b19b9a0a1a756b20
Document Type :
article
Full Text :
https://doi.org/10.3389/fbioe.2024.1454728