Automated Teeth Segmentation in Three-Dimensional Dental Images using a Hybrid GoogLeNet-Residual connection Architecture with Attention Mechanism

Abstract

Automatic teeth segmentation in three-dimensional (3D) dental images is essential for aiding dental diagnosis, treatment planning, and research. This paper proposes a novel methodology for automated teeth segmentation in 3D images using a hybrid architecture based on GoogLeNet, residual network (ResNet), and attention mechanism. The proposed approach addresses the challenges posed by the complex anatomical structures and variations in image quality inherent in 3D dental imaging. Initially, the input 3D images undergo preprocessing steps to enhance quality and standardize the data. Subsequently, a modified version of GoogLeNet augmented with residual connections is employed to extract hierarchical features from the 3D dental images. By integrating residual connections, the network effectively captures fine-grained details while mitigating the vanishing gradient problem, thereby enhancing segmentation performance. Additionally, an attention mechanism is incorporated into the architecture to dynamically highlight informative regions relevant to teeth segmentation. This attention mechanism enables the model to focus on salient features while suppressing irrelevant background noise, leading to improved segmentation accuracy and robustness. The proposed methodology is evaluated on a comprehensive dataset of 3D dental images, encompassing diverse dental conditions and patient demographics. Quantitative metrics such as accuracy, precision, recall, and F1-score are utilized to assess segmentation performance and compare against state-of-the-art techniques. Experimental results demonstrate that the proposed approach achieves superior segmentation accuracy and generalization capability compared to existing methods. Moreover, the attention mechanism enhances the interpretability of segmentation results, providing valuable insights for clinical interpretation and diagnosis. Overall, the proposed methodology offers a promising solution for automating teeth segmentation in 3D dental images, with potential applications in computer-aided diagnosis, treatment planning, and dental research. Further validation studies involving clinical experts and real-world deployment are warranted to evaluate the practical utility and efficacy of the proposed approach in clinical settings.