用临床数据提升颞下颌关节盘分割精度，结果更准确可靠。
Anatomically Consistent TMJ Disc Segmentation via Semantic Anchoring and Clinical Priors
- 通过语义锚点定位关节盘，结合临床指标优化边界
- 在2488例数据上实现最高4.96的Dice提升
- 适合医学影像分析与口腔颌面疾病诊断研究者
从MRI中分割颞下颌关节（TMJ）盘对内部紊乱的准确诊断至关重要，但因其体积小、对比度低及形态差异大，现有方法常产生碎片化或解剖不一致的分割结果，导致下游诊断中盘位置与形状测量不稳定。为此，我们提出TISC框架，融合语义锚定与临床元数据引导的边界精修。该框架首先通过原型语义锚定（PSA）模块，在基础模型特征空间中聚合相邻切片的MedDINOv3特征，生成基于原型的相似性图以实现稳健的盘体定位；随后通过临床元数据点精修（C-MPR）模块，以口部张开限度（MOL）这一与盘移位无复位相关的临床指标调节点级预测，进行靶向边界优化。在包含2,488例来自1,300名患者的PD MRI数据的大规模队列上，本方法在多种架构下相较强基线最高实现4.96的Dice提升，输出更具解剖一致性与临床可靠性的关节盘分割结果。
原文摘要 · Abstract (English)
Segmenting the temporomandibular joint (TMJ) disc from MRI is essential for accurate diagnosis of internal derangement, yet it remains unreliable in practice due to its small size, low contrast, and morphological variability. Existing methods, primarily adapted from general segmentation architectures, often produce fragmented or anatomically inconsistent masks, leading to unstable measurements of disc position and shape for downstream diagnosis. To address these challenges, we propose TISC, a TMJ disc segmentation framework that integrates semantic anchoring with clinical metadata-guided boundary refinement. The framework first establishes robust disc localization in the foundation model feature space via a Prototypical Semantic Anchoring (PSA) module that aggregates adjacent-slice MedDINOv3 features and derives a prototype-driven similarity map. It then performs targeted boundary refinement through a Clinical-Metadata Point Refinement (C-MPR) module, with point-wise predictions modulated by Mouth Open Limitation (MOL), a clinical indicator associated with disc displacement without reduction. On a large-scale cohort of 2,488 PD MRI volumes from 1,300 patients, our method achieves up to a 4.96 Dice improvement over strong baselines across diverse architectures, delivering more anatomically coherent and clinically reliable TMJ disc segmentation.
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