双域多尺度状态空间网络下的口腔颌面全景X射线图像分割算法研究

李冰; 胡伟杰; 刘侠

doi:10.11999/JEIT250639

双域多尺度状态空间网络下的口腔颌面全景X射线图像分割算法研究

doi: 10.11999/JEIT250639 cstr: 32379.14.JEIT250639

1.
哈尔滨理工大学自动化学院哈尔滨 150080
2.
黑龙江省复杂智能系统与集成重点实验室哈尔滨 150080

基金项目: 国家自然科学基金(61672197)，黑龙江省自然科学基金(LH00F035)

详细信息

作者简介:
李冰：女，博士，教授，研究方向为医学图像处理与分析，网络控制系统分析与综合

胡伟杰：女，硕士生，研究方向为医学图像处理与分析、模式识别

刘侠：男，博士，教授，研究方向为医学图像处理、模式识别、机器学习

通讯作者:
胡伟杰　huweijie0917@163.com

中图分类号: TN911; TP391.4; TP751; TP317.4
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- 文章访问数: 87
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- 被引次数: 0
出版历程
- 收稿日期: 2025-07-07
- 修回日期: 2025-12-22
- 录用日期: 2025-12-22
- 网络出版日期: 2025-12-29

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

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)

摘要

摘要: 针对口腔颌面全景X射线图像中存在的形态变异显著、牙体－牙龈边界模糊以及牙周组织灰度值重叠等问题，该研究提出基于双域多尺度状态空间网络的口腔颌面全景Ｘ射线图像分割算法。空间域利用视觉状态空间块建立牙弓动态传播模型，并利用微分方程实现跨象限长程关联捕捉。特征域构建可变形多尺度注意力金字塔，并利用通道－空间注意力动态加权关键解剖标志的灰度渐变特征，解析牙体－牙龈模糊边界。双域特征进一步通过三重注意力融合机制，强化解剖标注的语义表达。实验表明，该算法在颌面全景Ｘ射线图像分割任务中取得显著效果，戴斯系数(Dice)达93.8%，豪斯多夫距离(HD95)为18.73像素，充分验证了算法的有效性。
- 口腔颌面全景X射线图像分割 /
- 视觉状态空间块 /
- 可变形多尺度注意力金字塔 /
- 三重注意力融合
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.
- Oral and maxillofacial panoramic X-ray image segmentation /
- Visual state space blocks /
- Deformable multiscale attention pyramids /
- Triple attention fusion

HTML全文

图 1 双域多尺度状态空间网络

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图 3 选择性扫描模块工作机制

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图 2 视觉状态空间块

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图 4 可变形多尺度注意力金字塔

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图 5 空间与通道注意力协同模块

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图 6 三重注意力融合机制

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图 7 口腔颌面全景X射线数据集

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图 8 消融实验结果图

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图 9 常规场景与挑战性场景下的分割效果

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图 10 各网络分割效果对比

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图 11 各网络指标对比

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表 1 消融实验指标对比

	Dice(%)	HD95	Accuracy(%)	Recall(%)	Precision(%)	模型大小(MB)	参数量(M)
baseline	88.17	21.70	93.03	87.97	86.73	84.24	93.19
baseline_A	89.41	22.01	93.67	88.24	87.07	79.23	88.43
baseline_B	89.16	22.10	93.74	88.32	87.42	81.15	89.16
baseline_C	89.73	21.53	93.86	88.87	87.13	78.59	85.63
baseline_AB	91.32	20.71	94.07	89.03	87.26	82.15	91.52
baseline_AC	91.24	19.82	94.20	89.24	87.34	83.41	92.31
baseline_BC	91.53	19.77	94.13	89.37	87.42	81.68	90.74
Ours	93.86	18.73	94.57	90.46	88.03	81.23	90.10

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表 2 对比实验指标对比

模型名称	Dice (%)	HD95 (mm)	Accuracy (%)	Recall (%)	Precision (%)	模型大小 (MB)	参数量 (M)	GFlops (G)	FPS
TransUnet	91.43	24.13	92.02	88.76	86.31	82.14	88.52	29.34	18.24
Swin-Unet	89.03	24.63	92.63	89.13	86.81	84.61	92.31	40.93	11.47
VM-Unet	89.63	23.07	93.08	89.47	87.23	83.15	90.74	6.87	34.71
DA-TransUnet	91.25	20.29	93.24	89.73	87.78	87.78	80.63	31.42	16.87
Ege-Unet	88.18	32.64	92.57	86.93	86.04	52.87	61.91	13.73	27.38
TinyU-Net	86.27	35.83	91.74	86.02	85.94	48.65	58.31	4.18	45.63
SAMUS	88.51	31.83	95.02	88.83	86. 57	162.17	183.19	51.62	4.53
本文	93.86	18.73	94.30	90.46	88.03	81.23	90.10	24.93	22.14

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