PDF(Pubmed)
Abstract：
The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494.
摘要：
将自动分割方法结合到牙科X射线图像中，通过促进细致，完善了临床诊断和治疗计划的范例，牙齿结构和邻近组织的像素级关节。这是早期病理检测和细致的疾病进展监测的支柱。尽管如此，由于X射线成像的内在局限性，传统的分割框架经常会遇到重大挫折，包括受损的图像保真度，结构边界的模糊划定，以及牙髓等牙齿成分的复杂解剖结构，搪瓷,还有牙本质.为了克服这些障碍，我们提出了可变形卷积和Mamba集成网络，创新的2D牙科X射线图像分割架构，合并了一个合并结构可变形编码器，认知优化的语义增强模块，和分层收敛解码器。总的来说，这些组件支持多尺度全球功能的管理，加强特征表示的稳定性，并完善特征向量的合并。对14个基线的比较评估强调了其有效性，记录骰子系数增加了0.95％，第95个百分位数Hausdorff距离减少到7.494。
