Generating intermediate slices with U-nets in craniofacial CT images

Aim: The Computer Tomography (CT) imaging equipment varies across facilities, leading to inconsistent image conditions. This poses challenges for deep learning analysis using collected CT images. To standardize the shape of the matrix, the creation of intermediate slice images with the same width is necessary. This study aimed to generate inter-slice images from two existing CT images. Materials and Methods: The study utilized CT images from the Japanese Facial Bone Fracture CT Collection Project. The pixel values were converted to Hounsfield numbers and normalized. Three re-slice systems utilizing U-nets were developed: 1/3, 1/4, and 1/5. The datasets were divided into training and validation sets, and data augmentation techniques were applied. The U-net models were trained for 200 epochs. Validation was conducted using validation datasets. The generated images were compared to the corresponding original images using peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and mean squared error (MSE) calculations. Results: Statistical analysis revealed significant differences between linear interpolation and U-net prediction in all indexes. Conclusion: The developed re-slice systems with U-net models showed practical value for making intermediate slice images from the existing images in the craniofacial area. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Ethics Review Board of Hyogo Medical University (No.3326) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors