2026-10-14T21:32:41Z https://data.mendeley.com/oai

oai:data.mendeley.com/fgjd95tcr8.1 2025-12-18T07:43:22Z

Wuttisarnwattana, Patiwet Photorealistic Synthesis of Oral Lichen Planus Lesions: Code and Example Dataset Mendeley Data This dataset accompanies the manuscript "Photorealistic Synthesis of Oral Lichen Planus and Lichenoid Lesions Enhances Deep-Learning Segmentation in Intra-Oral Photographs" submitted to Computerized Medical Imaging and Graphics. Research Context: Medical image analysis for oral potentially malignant disorders (OPMDs) is hindered by limited annotated datasets. This work addresses data scarcity by combining CycleGAN with a Realistic Enhancement Algorithm (REA) to synthesize photorealistic oral lichen planus/lichenoid lesion (OLP/OLL) images on healthy buccal mucosa photographs. What This Dataset Contains: 1. codes.zip — Source code for the CycleGAN+REA synthesis pipeline: buccal mucosa segmentation, CycleGAN lesion generation, REA histogram matching, Laplacian pyramid blending, QC screening, and segmentation inference scripts. 2. models.zip — Pretrained weights: CycleGAN generator, TensorFlow-based buccal mucosa segmentation, OLP lesion segmentation (QC), and three Experiment 1 architectures (MANet+MiT-B2, FPN+MiT-B0, PSPNet+EfficientNet-B0). 3. data.zip — De-identified example images: healthy buccal mucosa inputs and real lesion images with ground-truth masks. Key Findings: * REA combines masked histogram matching with Laplacian pyramid blending to reduce CycleGAN artifacts (color drift, boundary discontinuities) * 520 dental professionals achieved only 58.22% discrimination accuracy (chance=50%), indicating high realism * Synthetic augmentation improved segmentation by up to +2.08 mIoU points How to Use: 1. Unzip codes.zip, data.zip, and models.zip 2. Install dependencies from requirements_simplified.txt 3. Run realistic_lesion_pipeline.py for synthesis 4. Run oral_lesion_segmentation.py for segmentation inference Data Restrictions: Due to ethics restrictions, the full clinical dataset cannot be released. This repository provides minimal examples to demonstrate pipeline functionality. Associated Publication: Theppitak S, Wongsapai M, Jaidee E, Sakdapreecha C, Ittichaicharoen J, Pongsiriwet S, Warin K, Suebnukarn S, Wuttisarnwattana P. "Photorealistic Synthesis of Oral Lichen Planus and Lichenoid Lesions Enhances Deep-Learning Segmentation in Intra-Oral Photographs", Computerized Medical Imaging and Graphics. [Under Review] Oral Medicine Lichen Planus Oral Cancer Computer-Aided Diagnosis Deep Learning Data Augmentation Dataset doi:10.17632/fgjd95tcr8.1 oai:data.mendeley.com/fgjd95tcr8.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/fgjd95tcr8 2025-12-18T07:43:22Z