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Updated: Jun 19, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Style harmonization of panoramic radiography using deep learning.

Hak-Sun Kim^1,2, Jaejung Seol³, Ji-Yun Lee¹

¹Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea.

|October 29, 2024

Summary

This summary is machine-generated.

This study harmonized panoramic radiograph images from different dental equipment using CycleGAN. The AI model successfully standardized image styles, improving consistency for diagnostic use.

Keywords:

Computer Deep learning Neural networks Panoramic Radiographic image enhancement Radiography

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Area of Science:

Dental Imaging
Artificial Intelligence in Radiology
Image Processing

Background:

Panoramic radiographs are crucial for dental diagnostics.
Variations in image styles from different equipment can affect interpretation.
Standardizing image appearance is essential for consistent analysis.

Purpose of the Study:

To harmonize panoramic radiograph images from two different equipment types (Rayscan Alpha Plus and Pax-i plus) within a single institution.
To achieve a consistent image style across diverse equipment.
To evaluate the effectiveness of CycleGAN for image style harmonization.

Main Methods:

Utilized CycleGAN to harmonize 7545 Pax-i plus (P-unit) images to match the style of 8079 Rayscan Alpha Plus (R-unit) images.
Employed objective metrics: Frechet Inception Distance (FID) and Learned Perceptual Image Patch Similarity (LPIPS).
Conducted expert evaluation by two oral and maxillofacial radiologists on harmonized and original images.

Main Results:

Transformed P-unit images showed lower FID (7.362) and LPIPS (0.488) compared to original P-unit images (8.380, 0.519).
A significant reduction in LPIPS (p < 0.05) was observed after harmonization.
Radiologists identified 43.3-46.7% of transformed P-unit images as matching the R-unit style.

Conclusions:

CycleGAN demonstrates potential for harmonizing panoramic radiograph image styles.
The AI model effectively reduced image style discrepancies between different equipment.
Further model enhancement is recommended for broader application with additional imaging units.