## 4 Discussion  
Accurate segmentation and identification of teeth in CBCT images is intricate and challenging. Although manual and semi-automatic methods can accomplish segmentation tasks, the accuracy and robustness of segmentation outcomes need improvement. AI-based methods for tooth segmentation in CBCT images represent a promising avenue for future development. Despite this potential, current research methods and results are scattered. This study attempts to provide a complete and systematic review of research published on this topic to date, to summarize current adopted AI tooth segmentation techniques, and to establish a foundation for further development. Depending on the type of output result, segmentation methods can be classified into semantic segmentation and instance segmentation. In this review, 15 studies performed semantic segmentation and 19 studies performed instance segmentation of CBCT images. Semantic segmentation methods can categorize each pixel or voxel in a CBCT image as a tooth or non-tooth but cannot distinguish between different instances of teeth [62]. [PERSON] et al. utilized the Mixed-Scale Dense (MS-D) CNN for semantic segmentation of the mandible and teeth. The segmentation results achieved a large overlap with the ground truth and exhibited minor surface deviation [30]. [PERSON] et al. proposed a 3.5D U-Net structure and compared it with five other U-Net structures, showing better semantic segmentation results [40]. On the other hand, instance segmentation methods can simultaneously detect and segment each tooth instance in CBCT images with its location and class information [26]. For example, ToothNet was the first implementation of using a two-stage deep CNN for tooth recognition and instance segmentation of CBCT images. It first extracts edge maps from CBCT images and then utilizes a 3D RPN and a novel learned similarity matrix based on Mask R-CNN to generate candidate regions [60]. [PERSON] et al. also used a two-level hierarchical deep learning method to first determine the tooth centers from thermal maps, and then identify the teeth and segment them into seven different types by DenseASPP-UNet [58]. Comparing different methods, we found that instance segmentation techniques typically utilize two-stage or multi-stage neural networks to accomplish the recognition and segmentation of teeth. Introducing a priori knowledge or morphological information, such as the center, skeleton, boundary, and curvature of the tooth, enhances the robustness and detailed representation of tooth segmentation. For example, the first stage involves detecting the edges or centers of the teeth to form the region of interest for coarse segmentation. The second stage or the subsequent process uses neural networks to accurately segment and recognize different instances of the teeth for fine segmentation [34; 48; 49; 50; 52; 58]. U-Net is a CNN-based architecture designed to classify each pixel in an image. It is a predominant model for deep learning-based segmentation and is extensively employed in segmenting teeth from CBCT images. The structure features symmetric encoders and decoders, enhanced by cross-layer connections that merge feature maps from different resolutions, preserving detailed information [24]. [PERSON] et al. performed an initial CBCT image segmentation using U-Net and further compared the results of six pre-trained deep learning networks in tooth classification [47]. Building upon U-Net, V-Net introduces residual modules into the encoder and decoder design and uses convolutional layers instead of pooling layers to increase network depth and complexity. This adjustment improves gradient flow during training and allows direct handling of 3D data, marking a significant step forward in volumetric image analysis [63]. [PERSON] et al. used V-Net and weakly labeled data to obtain coarse segmentation results, further training the model using accurately labeled datasets, and achieving high-resolution segmentation of individual teeth [53].