用于颌骨囊肿图像分割的带残差模块与注意力机制的多层特征聚合网络

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• Open access
• Published: 14 June 2026

• Huixia Zheng1,
• Xiaoliang Jiang2&
• Xu Xu1

_Scientific Reports_ (2026) Cite this article

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Abstract

Medical image segmentation plays a critical role in diverse healthcare applications, providing essential support for early disease detection and comprehensive treatment planning. However, due to blurred boundaries, low contrast between lesions and surrounding tissues, and variations in lesion morphology and scale, existing segmentation methods still face significant challenges in enlarging the receptive field and extracting discriminative image features. To address these challenges, we propose MFA-Net, an innovative architecture specifically designed for accurate medical image segmentation. During the encoding stage, we employ convolutional kernel structures composed of 3$\times$1 followed by 1$\times$3 and 9$\times$1 followed by 1$\times$9 to construct the residual module, aiming to mitigate issues associated with the gradient vanishing and network performance degradation. In addition, we introduce a sophisticated channel-spatial attention mechanism in the last encoder to improve the capacity for feature extraction and representation. Furthermore, feature maps from adjacent encoder units are utilized to construct a multi-layer feature aggregation block, which allows features from different scales to synergistically contribute to the overall representation. Finally, to comprehensively evaluate the effectiveness and robustness of our approach, we conduct extensive experiments on two clinically significant jaw cyst datasets and the publicly available ISIC-2018 dataset. Comparative results demonstrate that MFA-Net outperforms current state-of-the-art methods across multiple evaluation metrics, achieving F1 of 0.9411, IoU of 0.9444, Mcc of 0.9406, and Jaccard of 0.8892 on the original jaw cyst dataset. Similarly, on the augmented jaw cyst dataset derived from the original jaw cyst images, MFA-Net maintains superior performance, achieving F1 of 0.9228, IoU of 0.9280, Mcc of 0.9221, and Jaccard of 0.8579. On the ISIC-2018 dataset, MFA-Net also achieves F1 of 0.8678, IoU of 0.8388, Mcc of 0.8238, and Jaccard of 0.7783. Moreover, the ablation study demonstrates the important contributions of the residual structure, channel-spatial attention mechanism, and multi-layer feature aggregation block to the overall performance of MFA-Net.

Acknowledgements

Thanks to the editorial team and all the anonymous reviewers who helped us improve the quality of this paper.

Funding

This work was supported by the National Natural Science Foundation of China (No. 62102227), Joint Fund of Zhejiang Provincial Natural Science Foundation of China (No. LTGC23E050001), Science and Technology Major Projects of Quzhou (2023K211, 2025K061).

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Authors and Affiliations

1. Department of Stomatology, Quzhou People’s Hospital; The Quzhou Affiliated Hospital, Wenzhou Medical University, Quzhou, 324000, Zhejiang, China

Huixia Zheng&Xu Xu

1. College of Mechanical Engineering, Quzhou University, Quzhou, 324000, China

Xiaoliang Jiang

Authors

1. Huixia Zheng
2. Xiaoliang Jiang
3. Xu Xu

Corresponding author

Correspondence to Xu Xu.

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Competing interests

The authors declare no competing interests.

Ethics statement

All experimental protocols were approved by the ethics committee of Quzhou People’s Hospital.

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Zheng, H., Jiang, X. & Xu, X. Multi-layer feature aggregation network with residual module and attention mechanism for jaw cyst image segmentation. _Sci Rep_ (2026). https://doi.org/10.1038/s41598-026-56281-3

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• Received: 10 October 2024

• Accepted: 29 May 2026

• Published: 14 June 2026

• DOI: https://doi.org/10.1038/s41598-026-56281-3

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