# Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images
## 2 Materials and Methods  
### Data  
We utilized four datasets: two of them from different centers (CenterA and CenterB) including mandible and maxilla segmentations and two public datasets (PDDCA and TCIA) for OAR segmentation in H&N CT. The CenterA dataset was randomly divided into training, validation, and test datasets, consisting of 146, 10, and 15 sets at the patient level, respectively. The training dataset was used to train the models, whereas the validation dataset was used to tune the hyperparameters of the models and check the validity of the training process. The test dataset from CenterA and other datasets were completely separated from the training and validation datasets, and were used for evaluating the performance of the models. Specifically, the PDDCA, TCIA, and CenterB datasets are external datasets that were used to evaluate the models for dataset compatibility. Detailed data characteristics of all datasets, including the number and size of slices, pixel spacing, and slice thicknesses, are presented in Table 1.  
CenterA and CenterB datasets include CT images and the corresponding segmentation of the mandible and maxilla provided by the clinical experts of oral and maxillofacial surgery department and orthodontic department, respectively. Targets in CenterA datasets were delineated manually by an expert surgeon (B.Y.H.) from Kyung Hee University Hospital, Seoul, Korea. Ethical approval was received from the institutional review board (IRB) (approval number KH-DT19033) for CenterA dataset. CenterB dataset was built with 15 sets of dental CBCT (i-CAT 17-19 TM, Imaging Science International) from Chungang University Hospital, Seoul, Korea (approval number 1922-007-362). Those CT images were segmented by two well-trained biomedical engineers supervised by a clinical expert. The PDDCA dataset is a public dataset for OAR segmentation in the H&N region of CT images released at the 2015 MICCAH&N radiotherapy OAR segmentation challenge [8] provided and maintained by Dr. [PERSON] at Harvard Medical School. The CT scans in the dataset are available via the Cancer Imaging Archive (TCIA) and are originally from the radiation therapy oncology group (RTOG) 0522 study, which includes multi-institutional clinical studies from patients with stage III or IV H&N carcinoma [42]. The dataset consists of 48 H&N CT images with nine OAR structures manually re-segmented by experts for uniform quality and consistency. In the challenge, the dataset was divided into 25 training sets, 10 off-site test sets, and 5 on-site test sets. In this study, we employed 15 test sets with mandible annotation. The TCIA dataset [43] contains 31 CT scans from TCIA [44] and segmentations for 21 OARs, in which we only used mandible segmentation. They were delineated by an experienced radiographer, with additional peer arbitration by another radiographer and a radiation oncologist. Both the PDDCA and TCIA datasets include a selected part of the Head-Neck Cetuximab open source dataset [45]; owing to different selection criteria and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **CenterA** & **PDDCA †** & **TCIA †** & **CenterB** \\ \hline No. of sets & 171 & & & \\  & (Train: 146, Validation: 10, & 15 & 28 & 15 \\  & Test: 15) & & & \\ Acquisition type & MDCT & MDCT & MDCT & CBCT \\ Target structure & Mandible \& Maxilla & OARs & OARs & Mandible \& Maxilla \\ No. of slices & 166–450, & 109–263, & 61–110, & 432 \\  & 208 + 32 & 154 \(\pm\) 36 & 93 \(\pm\) 12 & 432 \\ Slice size [pixel] & 512 & 576 & 512 & 512 \\ Pixel spacing [mm] & 0.36–0.49, & 0.98–1.27, 1.11 \(\pm\) 0.10 & 0.94–1.27, 1.04 \(\pm\) 0.10 & 0.40 \\  & 0.50–1.04, & & & \\  & 0.99 \(\pm\) 0.07 & 2.0–3.0, 2.73 \(\pm\) 0.31 & 2.50 & 0.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Properties of the datasets. different train/validation/test set division, there are five scans present in both PDDCA and TCIA test sets.