# Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images
## 1 Introduction
(**a**) Metal artifacts caused by dental implants (**b**) Difficulty in distinguishing mandibular and maxillary teeth, or mandible and midface (**c**) Low intensity and thin edges in condy (**d**) Inter-patient anatomical variance. with three convolution layers. [PERSON] et al. [21] then incorporated a CNN with the pretrained shape representation model. Beyond simple CNNs, U-Net [22] has been one of the most popular CNNs for medical image segmentation. Compared with other CNNs, U-Net, with a simple and flexible structure, shows an outstanding performance in segmentation extracting image features by multi-scale recognition and fusion [23]. Several approaches have been developed by applying the U-Net structure as a baseline for mandible segmentation. [PERSON] et al. [1] used three U-Nets for orthogonal planes with dice loss to segment the mandible. AnatomyNet [13] was proposed to segment OARs from H&N CT, which was built on a 3D U-net architecture. A two-stage segmentation framework for OAR in CT was also proposed, which employs two 3D U-Nets for localization and segmentation [24]. Several studies have utilized U-Net with other structures together as well. Both a faster regional CNN and attention U-Net for localization and segmentation have been introduced by [PERSON] et al. [24]. A recurrent segmentation CNN was proposed that embeds the CNN into a recurrent neural network for segmentation of the mandible from CT [7]. An attention mechanism, which has been advanced with deep learning models in computer vision, has been incorporated to U-Net for segmentation. Squeeze-and-excitation blocks were incorporated into U-Net for prostate zonal segmentation of multi-institutional MRI datasets, enhancing both intra- and cross-dataset generalization [25]. An attention gate model that can be integrated into CNN models was proposed to automatically learn to focus on target structures [26]. Focus U-Net with attention gate for spatial and channel-based attention was proposed for fast and accurate polyp segmentation [27]. However, there is an inevitable and considerable pitfall in data-driven methods including deep learning, which is the lack of data compatibility; that is, the method may fail to accurately segment images with varying properties, such as those acquired using different CT scanners and imaging protocols [28]. The compatibility of dataset in the models refers to the ability of models to inference the input images that have different distributions in the latent space from the multi-center training dataset [28]. In general, datasets are limited so that they cannot fully represent the general patient population in the clinic [29]. As a result, models trained on the specific center domain do not perform well on a different center domains with disparate data distribution [30]. This drawback is more significant when applying deep learning clinically on images from other institutions. For example, it is known that the Hounsfield units measurement varies between scanners [31]. The results of models targeted to CT can vary depending on the imaging parameters, the scanner type, calibration, or the scan date [29; 32; 33]. That is, multicenter data tend to have different data distributions, making trained neural network impractical. With consideration for this variability, it has been recently been required to test the artificial intelligence model with an external dataset [32]. From these limitations in clinical applications, data compatibility in deep learning for medical images has been an essential challenge to be addressed. To solve this problem, research has been conducted to utilize multicenter data in neural network training [33; 34]. Another potential solution to this problem is transfer learning [35; 36; 37], which trains with more easily obtained datasets from different domains to enhance performance [38]. However, these approaches have limitations for clinical use, as available medical data are scarce compared to natural images and are not sufficient for deep learning.