Oral teeth image segmentation plays an important role in teeth orthodontic surgery and implant surgery. As the tooth roots are often surrounded by the alveolar, the molar’s structure is complex and the inner pulp chamber usually exists in tooth, it is easy to over-segment or lead to inner edges in teeth segmentation process. In order to further improve the segmentation accuracy, a segmentation algorithm based on local Gaussian distribution fitting and edge detection is proposed to solve the above problems. This algorithm combines the local pixels’ variance and mean values, which improves the algorithm’s robustness by incorporating the gradient information. In the experiment, the root is segmented precisely in cone beam computed tomography (CBCT) teeth images. Segmentation results by the proposed algorithm are then compared with the classical algorithms’ results. The comparison results show that the proposed method can distinguish the root and alveolar around the root. In addition, the split molars can be segmented accurately and there are no inner contours around the pulp chamber.
Due to the significant non-stationarity and feature distribution discrepancies in surface electromyography (sEMG) signals during muscle fatigue monitoring, traditional fixed-parameter Transformer models often struggle to accurately capture the complex evolution of time-frequency characteristics across different fatigue stages. To address this limitation, this paper proposes a K-means clustering-guided neural architecture search method (CG-NAS) to achieve adaptive optimization of Transformer architectures based on data distribution characteristics. The method first classified input EMG features using the K-means clustering algorithm and constructed Gaussian distributions characterized by mean and variance to quantify the complexity of each cluster. These distribution priors then guided the neural architecture search process, enabling dynamic alignment between the architecture search space and data characteristics: for low-complexity data clusters with small mean and variance, lightweight Transformer architectures were selected, whereas for high-complexity clusters, architectures with greater width and depth were allocated. Experimental results demonstrated the superior performance of CG-NAS in muscle fatigue index prediction tasks, achieving a mean absolute error of 0.098 2 and a coefficient of determination of 0.957 3, significantly outperforming multiple benchmark models. The study shows that CG-NAS effectively aligns with the nonlinear evolution of time-frequency features during the fatigue process and provides an efficient and robust solution for fatigue monitoring.