A Dental Fluorosis Segmentation Model Combining Dynamic Snake Convolution and Learnable Shape Prior

ABSTRACT As one of the most typical clinical manifestations of fluorosis, accurate segmentation of dental areas is quite significant for early disease assessment, grading diagnosis, and clinical intervention. However, traditional manual examination is highly subjective and often leads to misdiagnosis or missed diagnosis. Existing deep learning segmentation methods face challenges such as the variable morphology of dental fluorosis, indistinct edges, and the lack of explicit geometric constraints. To overcome these challenges, we propose a dental fluorosis segmentation model, DSSL‐UNet, based on the U‐Net backbone, integrating dynamic snake convolution (DSConv) and a learnable shape prior module (LSPM). DSConv introduces a learnable offset field that adaptively deforms convolutional kernels along curvilinear tooth contours, thereby strengthening feature extraction for indistinct and tortuous edges. The LSPM consists of self‐updating blocks (SUB) and cross‐updating blocks (CUB), which model long‐range dependencies and local shape priors to improve the continuity and accuracy of segmentation masks. Comprehensive experiments on both public and custom dental fluorosis datasets demonstrate that DSSL‐UNet outperforms mainstream models across all metrics, providing more precise and complete segmentation of dental fluorosis. This model provides robust technical support for the automated and accurate diagnosis of dental fluorosis.