EGT-UNet: Evolutionary Game-Theoretic Adaptive Optimization for Pediatric Panoramic Tooth Segmentation
Bioengineering, vol.13, no.7, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 13 Issue: 7
- Publication Date: 2026
- Doi Number: 10.3390/bioengineering13070840
- Journal Name: Bioengineering
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, INSPEC, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Keywords: deep learning, EGT-UNet, panoramic radiography, pediatric tooth segmentation
- Yozgat Bozok University Affiliated: Yes
Abstract
The present work proposes EGT-UNet, an innovative loss optimization mechanism based on Evolutionary Game Theory (EGT) aimed at pediatric panoramic tooth segmentations. In the absence of a large set of high-quality images in the current literature, we developed a novel pediatric panoramic image database, including 1269 images from Ordu University Faculty of Dentistry. The dataset includes subjects aged 3–14, where 67% are males and 33% are females. The annotation of all images was done at the pixel level by a professional orthodontist with a two-fold verification process. In order to prevent data leakage, the dataset was split at the subject level into training/validation (85%) and independent test (15%) sets. Data in the training set were divided into folds for five-fold cross-validation. From a structural point of view, EGT-UNet is an improved version of U-Net, equipped with the following modules: Squeeze-and-Excitation blocks, Attention Gates, and a dilated convolutional module mimicking Atrous Spatial Pyramid Pooling. The main novelty of the proposed approach involves the application of the dynamic change in loss weights. Specifically, in our work, the fusion of three losses—Dice, Focal Tversky and Boundary—was dynamically tuned via replicator dynamics using task-specific fitness functions. The difference with traditional loss-weighting methods is that in the latter case, fixed weights are applied. To evaluate the effect of each component of the EGT-UNet, we conducted an ablation study of six architectures varying in terms of hybrid loss function and dynamic/static weight tuning. Using the independent test dataset, we observed a similar performance level of all models with Dice scores ~0.93. Our best model, called “EGT Aggressive”, achieved Dice = 0.931 ± 0.044, IoU = 0.873 ± 0.066, and Boundary Dice = 0.631 ± 0.064. Importantly, this model demonstrated a statistically significant superiority over the baseline network according to region-related metrics and boundary metrics (Wilcoxon p < 0.05). In computational studies, we revealed an increased model robustness when dealing with highly complex mixed dentition images.