## 4 Discussion
combined the advantages of semantic and instance segmentation and used a U-Net model for labeling and semantic segmentation of each pixel in the image [36]. [PERSON] et al. used a multi-task CNN with a U-Net structure to segment the foreground and center regions of a tooth first. Then both models followed by WT to segment the teeth into separate units, especially for overlapping teeth [34]. Gaussian distributions based on heatmap responses effectively preserved spatial information in the image through tooth surface pixel distributions, the task of pixel-by-pixel classification is converted to a distance map regression task to perform individual tooth segmentation, especially for overlapping teeth, with better segmentation results [58,61]. Moreover, the Region Proposal Network with Feature Pyramid Network can accurately locate the position of each tooth from the panoramic image and initially obtain the segmentation area, which improves the accuracy and edge smoothness of CBCT image segmentation and allows further effective segmentation of the pulp chamber [29,51,52]. Artificial intelligence models have also been used to assist traditional manual segmentation methods, such as LSM, by automatically locating the center point to control the segmentation range and achieve higher segmentation efficiency and accuracy [28,35,49]. Considering the existing AI methods used for tooth segmentation in CBCT images, the evaluation metrics used in the various studies varied widely and lacked a uniform standard. Most models have DSCs greater than 90% and accuracy ranging from 83% to 99%, whereas a true application of machine learning methods to clinical work requires an accuracy rate of approximately 99% [5]. However, AI-based image segmentation methods are affected by computer literacy, annotation cost and high diversity of medical images. Tooth voxels only account for 1-3% of the overall CBCT data, and many slices do not contain any tooth-related voxels, which makes direct processing of the entire CBCT image challenging. Therefore, most current deep learning models adopt a stage-based supervised learning approach, which consists of roughly detecting the tooth region and then performing fine segmentation. [PERSON] et al. used a pre-trained U-Net with multiple stages on 3 different CBCT sub-volumes, including tooth, tooth-containing slices, and the whole CBCT, but the segmentation results were not significantly improved [56]. Furthermore, supervised neural networks required to be trained with a large number of samples that have been manually annotated, but significant differences between different CBCT images, such as brackets, metal fillings, crown restorations, dental implants, artifacts, and missing tooth areas, increase the cost of labeling and affect the training results. Meanwhile, most studies use a relatively small number of samples, which increases the risk of model overfitting. Therefore, data augmentation is usually used to improve the robustness and generalization ability of the model. Examples include spatially rotating, scaling, and flipping the image [33], using blending modes to perform random linear combinations of images or cropping and re-collaging images [39]. In addition, researchers have also improved the model generalization ability to some extent by normalizing the neural network, such as employing the dropout layer, but it still cannot solve the problem fundamentally [55]. It is worth mentioning that the open CBCT annotated datasets Ctooth and Ctooth++ were established and made public in two studies [45,46]. They provide a basis for subsequently training network weights based on large-scale datasets. At the same time, the use of pre-trained neural networks may also improve the model's generalization and migration ability. Despite the development of artificial intelligence in the field of tooth segmentation, it is important to note that all current studies have a high risk of uncertainty.