用结构感知与知识引导的Mamba模型，自动评估颧上颌缝成熟度。
Structure-aware Knowledge-guided Heterogeneous Mamba for Zygomaticomaxillary Suture Assessment
- 分路径架构模拟正畸医生诊断流程，分离结构与语义信息。
- 在4-24岁共3790张图像上达到当前最佳准确率。
- 结合大模型描述与边缘增强，提升对细微变化的识别能力。
颧上颌缝是连接颧骨与上颌骨的关键环形结构，在上颌前移手术中起主要阻力作用，其成熟状态直接影响正畸干预的时间与效果。然而，由于缝线处高频细微变化以及相邻阶段间全局语义模糊，准确分期仍具挑战。为此，我们构建了首个公开的ZMS数据集，包含4至24岁共计3,790张图像。基于此，提出SKMamba——一种结构感知与知识引导的Mamba多模态框架，用于自动化评估ZMS成熟度。该模型采用解耦双路径架构，模拟资深正畸医生的分级诊断过程。引入隐式边缘提取器（IEE），利用结构预训练降低骨小梁噪声、增强缝合边界；同时设计跨模态语义对齐（CSA）模块，融合大语言模型（LLM）提供的解剖学描述，实现局部形态特征与全局语义信息的对齐，同时确保形态证据为主导决策依据。在自建数据集上的大量实验表明，SKMamba性能优于现有方法。代码已开源：https://github.com/galaxygxq1116/SKMamba。
原文摘要 · Abstract (English)
The Zygomaticomaxillary Suture is a key circummaxillary structure that connects the zygomatic bone and the maxilla, which serves as a primary site of resistance during maxillary advancement, and its maturation status directly influences the timing and efficacy of orthopedic interventions. However, accurate staging of ZMS maturation remains challenging due to subtle high-frequency transitions in suture lines and the global semantic ambiguity between adjacent stages. To address this, we present the first public ZMS dataset, comprising 3,790 ZMS images covering the entire age range from 4 to 24 years. Based on this dataset, we propose SKMamba, a Structure-aware and Knowledge-guided Mamba-based multi-modal framework for automated ZMS maturation assessment. SKMamba adopts a decoupled dual-path architecture that mimics the hierarchical diagnostic process used by experienced orthodontists. We first introduce an Implicit Edge Extractor (IEE), which leverages structural pre-training to reduce trabecular noise and accentuate sutural boundaries. Complementarily, a Cross-Modal Semantic Alignment (CSA) module is designed to incorporate anatomical descriptions from a large language model (LLM). This module helps align local morphological cues with global semantic descriptions while ensuring that objective morphological evidence remains the primary basis for decisions. Extensive experiments on our ZMS dataset demonstrate that SKMamba achieves state-of-the-art performance compared to existing methods. Code is available at https://github.com/galaxygxq1116/SKMamba.
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