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

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)

Received Date: 2025-07-07
Accepted Date: 2025-12-22
Rev Recd Date: 2025-12-22

Available Online: 2025-12-29

Abstract

Abstract

Objective To address significant morphological variability, blurred boundaries between teeth and gingival tissues, and overlapping grayscale distributions in periodontal regions of oral and maxillofacial panoramic X-ray images, a state space model based on Mamba, a recently proposed neural network architecture, is adopted. The model preserves the advantage of Convolutional Neural Networks (CNNs) in local feature extraction while avoiding the high computational cost associated with Transformer-based methods. On this basis, a Dual-Domain Multiscale State Space Network (DMSS-Net)-based segmentation algorithm for oral and maxillofacial panoramic X-ray images is proposed, resulting in notable improvements in segmentation accuracy and computational efficiency. Methods An encoder–decoder architecture is adopted. The encoder consists of dual branches to capture global contextual information and local structural features, whereas the decoder progressively restores spatial resolution. Skip connections are used to transmit fused feature maps from the encoding path to the decoding path. During decoding, fused features gradually recover spatial resolution and reduce channel dimensionality through deconvolution combined with upsampling modules, finally producing a two-channel segmentation map. Results and Discussions Ablation experiments are conducted to validate the contribution of each module to overall performance, as shown in Table 1. The proposed model demonstrates clear performance gains. The Dice score increases by 5.69 percentage points to 93.86%, and the 95th percentile Hausdorff distance (HD95) decreases by 2.97 mm to 18.73 mm, with an overall accuracy of 94.57%. In terms of efficiency, the model size is 81.23 MB with 90.1 million parameters, which is substantially smaller than that of the baseline model, enabling simultaneous improvement in segmentation accuracy and reduction in parameter count. Comparative experiments with seven representative medical image segmentation models under identical conditions, as reported in Table 2, show that the DMSS-Net achieves superior segmentation accuracy while maintaining a model size comparable to, or smaller than, Transformer-based models of similar scale. Conclusions A DMSS-Net-based segmentation algorithm for oral and maxillofacial panoramic X-ray images is proposed. The algorithm is built on a dual-domain fusion framework that strengthens long-range dependency modeling in dental images and improves segmentation performance in regions with indistinct boundaries. The spatial-domain design effectively supports long-range contextual representation under dynamically varying dental arch morphology. Moreover, enhancement in the feature domain improves sensitivity to low-contrast structures and increases robustness against image interference.

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References(27)

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