Research on Segmentation Algorithm of Oral and Maxillofacial Panoramic X-ray Images under Dual-domain Multiscale State Space Network

LI Bing; HU Weijie; LIU Xia

doi:10.11999/JEIT250639

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LI Bing, HU Weijie, LIU Xia. Research on Segmentation Algorithm of Oral and Maxillofacial Panoramic X-ray Images under Dual-domain Multiscale State Space Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250639

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LI Bing, HU Weijie, LIU Xia. Research on Segmentation Algorithm of Oral and Maxillofacial Panoramic X-ray Images under Dual-domain Multiscale State Space Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250639

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Research on Segmentation Algorithm of Oral and Maxillofacial Panoramic X-ray Images under Dual-domain Multiscale State Space Network

doi: 10.11999/JEIT250639 cstr: 32379.14.JEIT250639

LI Bing^{1, 2},
HU Weijie^{1, 2
,
,},
LIU Xia^{1, 2}

1.
School of Automation, Harbin University of Science and Technology, Harbin 150080, China
2.
Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin 150080, China

Funds: The National Natural Science Foundation of China (61172167), Heilongjiang Provincial Natural Science Foundation Project (LH00F035)

Accepted Date: 2025-12-22
Rev Recd Date: 2025-12-22

Available Online: 2025-12-29

Abstract

Abstract

Objective To address significant morphological variations, blurred boundaries between teeth and gums, and overlapping gray levels in periodontal tissues within panoramic X-ray images of the oral and maxillofacial region, this paper introduces a state-space model based on Mamba, a recent neural network architecture. This model retains the advantage of Convolutional Neural Networks (CNNs) in extracting local image features while avoiding the high computational complexity of Transformers. Building on this, the study proposes a dual-domain multiscale state space network-based segmentation algorithm for panoramic X-ray images of the oral and maxillofacial region, ultimately achieving significant improvements in segmentation accuracy and model efficiency. Methods This model adopts a network architecture primarily based on encoding-decoding, comprising a dual-branch encoder for acquiring global and local information, a decoder for feature restoration, and skip connections for conveying fused encoding path feature maps. During the decoding phase, fused features gradually recover resolution and reduce channel count through deconvolution combined with sampling modules, ultimately outputting a 2-channel segmentation map. Results and Discussions This study validated the contribution of each module to model performance through ablation experiments, as shown in Table 1. This model achieved significant performance improvements: in segmentation accuracy, the Dice score increased by 5.69 percentage points to 93.86%, while the HD95 value decreased by 2.97 mm to 18.73 mm, achieving 94.57% accuracy. Regarding model efficiency, the model has a size of only 81.23 MB with 90.1 million parameters, which is significantly lower than that of the baseline model, achieving synergistic optimization of accuracy improvement and model compression. When compared with seven mainstream medical image segmentation models under identical experimental conditions, as shown in Table 2, DMSS-Net achieves higher segmentation accuracy while maintaining a model size comparable to or smaller than that of similarly scaled Transformer models. Conclusions This paper proposes a dual-domain multiscale state-space network-based segmentation algorithm for panoramic X-ray images of the oral and maxillofacial region. The algorithm centers on a dual-domain fusion network framework. This framework significantly enhances the model’s ability to capture long-range dependencies in dental images and enables precise segmentation of blurred boundaries. An innovative spatial domain design helps address the challenge of capturing long-range context amid the dynamic dental arch morphology. In addition, a mechanism that enhances the feature domain not only improves the detection of low-contrast features but also increases robustness against interference.

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References(27)

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