Region-Specific Attention-Enhanced U-Net: Architectural Optimization for Small-Target and Weak-Boundary Segmentation in TMJ MRI.

The diagnosis of temporomandibular joint (TMJ) anterior disc displacement primarily relies on magnetic resonance imaging (MRI). Clinical assessment depends heavily on manual segmentation by radiologists, which is time-consuming and highly subjective. We aimed to develop and validate a novel PE AttU-Net for accurate automatic multi-structure segmentation of the articular disc and condyle on TMJ MRI images. The proposed PE AttU-Net integrates attention mechanisms, boundary enhancement modules, and small-target optimization strategies to achieve joint multi-structure segmentation. A total of 512 TMJ MRI images from 334 patients covering five displacement grades (normal, slight, mild, moderate, severe) were collected and preprocessed uniformly. Model validation, quantitative evaluation, and comparative experiments were performed to verify the model's performance. Evaluated using the five-grade dataset, the model obtained satisfactory segmentation results for the articular disc. The Dice coefficients were 78.44%, 74.80%, 74.25%, 75.67%, and 75.31%, respectively. Comparative experiments demonstrate that the PE AttU-Net improves segmentation precision for small structures featuring weak boundaries, represented by the articular disc.