## 4 Discussion
Furthermore, by modifying the order of doubling the number of channels and the process of deconvolution in U-Net, 3D U-Net can speed up convergence and avoid bottlenecks in the network structure [64]. Virtual Patient Creator is an online cloud platform that offers 3D U-Net AI models as open-source data on its website, enabling high-resolution tooth segmentation of CBCT images [54]. Several studies have validated its performance by segmenting teeth with different conditions, such as teeth containing different fillings, small dedulous regions, and teeth with brackets, and obtained good segmentation accuracy [31; 39; 42]. Despite these advancements, achieving optimal segmentation solely with one deep learning approach remains challenging. U-Net struggles with long-range dependencies and global information extraction, particularly when the segmentation target has low contrast with the background. Furthermore, both V-Net and 3D U-Net are computationally and memory intensive due to their direct processing of 3D images. To solve these problems, researchers have proposed numerous enhancements to these foundational models to refine detection and segmentation outcomes. On the one hand, the multi-task strategy can be used to decompose the tooth segmentation problem into different sub-problems, such as detection and identification, coarse segmentation, fine segmentation, and classification, in order to improve accuracy and efficiency. [PERSON] et al. utilized a framework containing three task branches, including spatial embedding, seed mapping, and identification, while performing deep learning on CBCT images, for instance tooth segmentation and classification [37]. On the other hand, some studies have introduced attention or self-attention mechanisms to capture pixels' correlation and contextual information between or within teeth to improve the accuracy and consistency of tooth segmentation [65]. [PERSON] et al. inserted a 3D self-attention module into the encoder of V-Net, which can obtain the spatial relationship between pixel points in the tooth geometry, thus capturing the complete tooth spatial features and achieving an effective separation of the boundaries from the tooth root and bone [48]. [PERSON] et al. introduced a series of 3D attentional structures in U-Net to reduce the impact of background and noise on segmenting the tooth volume [45]. A recently developed deep learning network, Transformer, can extract more effective image features by attentively capturing global contextual information and long-range dependencies of the target [66]. Based on this idea, [PERSON] et al. proposed a pre-trained CNN Transformer Architecture UNet (CTA UNet), which combines the benefits of CNN and Transformer to segment CBCT images from multiple scales and merge dental spatial features effectively [32]. TransUNet [67] has a similar structure to CTA UNet, and CoTNET [68] is also developed based on it, which can simultaneously extract global and local fine features, combine with self-attention methods, and link context to achieve good target detection and instance segmentation performance. [PERSON] et al. combined CoTNet with U-Net++ (a U-Netvariant) to achieve feature fusion at different levels and multiple scales and obtain finer segmented images [33]. In addition, by combining other machine learning techniques and algorithms, the detection effect and segmentation results can be further optimized. The Watershed Transform (WT) is a segmentation algorithm that enables automatic segmentation and separation of individual teeth by analyzing topological morphology to obtain boundary information [69]. [PERSON] et al. proposed a two-branch V-Net that predicted both the probability of tooth region and the tooth surface, up-sampled the feature map at the decoder path and integrated the Marker-controlled watershed transform to achieve accurate segmentation of individual teeth and minimize the information loss [59]. [PERSON] et al.