Comparative Analysis of Deep Learning-Based Segmentation Models for Root Canal Filling Detection with Efficiency and Pruning Evaluation
18th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2026, Bucharest, Romanya, 2 - 03 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/ecai69016.2026.11613830
- Basıldığı Şehir: Bucharest
- Basıldığı Ülke: Romanya
- Anahtar Kelimeler: computational cost, deep learning, dentistry, pruning
- Gazi Üniversitesi Adresli: Evet
Özet
Accurate automated segmentation of root canal fillings in periapical radiographs represents a critical bottleneck in scaling endodontic quality assessment, yet most existing deep learning approaches prioritize accuracy while neglecting deployment feasibility in resource-constrained clinical settings. This study evaluates deep learning-based segmentation models for automated segmentation of root canal fillings in periapical dental radiographs. Five widely used architectures - U-Net, U-Net++, Feature Pyramid Network (FPN), LinkNet, and SegFormer - were compared in terms of both segmentation performance and computational efficiency using a dataset of 597 annotated periapical radiograph images. Experimental results demonstrated that U-Net achieved the highest segmentation accuracy with a mean Intersection over Union (IoU) of 74.57 and a Dice score of 85.42 across five independent training cycles. Lightweight models such as FPN and LinkNet provided a favorable balance between performance and efficiency for resource-constrained clinical environments, with LinkNet achieving a GPU latency of 7.52 ms and throughput of 132.9 images/s. Additionally, pruning experiments on the best-performing U-Net model revealed that moderate unstructured pruning led to limited performance degradation recoverable through fine-tuning, whereas aggressive pruning and structured pruning at higher ratios resulted in substantial accuracy loss. These results highlight the trade-off between segmentation accuracy and computational efficiency and provide practical insights for the deployment of deep learning models in clinical dental imaging workflows.