Conference Paper2025

Multi-Strategy Optimization of U-Net Variants for Orthopantomogram Segmentation

Shakib Absar, Iftekhar Ahmed, Shadman Sakib, Nabajyoti Sharma, Siam Tahsin Bhuiyan, Saadia Binte Alam, Ashraful Islam, Rashedur Rahman

2025 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)

IEEE, pp. 17-22

  • Good Health and Well-being — Relevance: 69%
DOI: 10.1109/becithcon69222.2025.11503995URL

Abstract

Segmentation of dental structures in panoramic radiographs is challenging due to anatomical variability, low contrast, and artifacts. We evaluate whether U-Net-based models, paired with a unified multi-strategy optimization pipeline, can deliver accurate and robust performance for this task. Using a curated dataset of 361 annotated orthopantomograms (OPGs), we benchmark five architectures: Vanilla U-Net, Attention U-Net, UNet++, SegNet, and TransUNet, and apply a single pipeline that combines CLAHE, edge enhancement, denoising, stochastic augmentation, and boundary-aware loss optimization to emphasize crisp boundaries and stable predictions. In our comparative evaluation, U-Net variants consistently lead. Vanilla U-Net achieves 91.38% Dice and 84.13% IoU, while UNet++ achieves 91.06% Dice and 83.59% IoU; thus, Vanilla U-Net is ahead by 0.32% in Dice and 0.54% in IoU, and both outperform the non-U-Net baselines. Step-wise ablation quantifies the marginal contribution of each strategy (∆Dice/∆IoU) and shows that the entire pipeline sharpens the boundaries and stabilizes performance in variations in image quality, patient age, and tooth count. Taken together, our comparisons and ablations establish U-Net variants augmented by our pipeline as a strong, reproducible baseline for panoramic dental segmentation and a reliable foundation for future model development.

Keywords

  • TransUNet
  • SegNet
  • boundary-aware loss
  • image denoising
  • U-Net
  • U-Net variants
  • edge enhancement
  • Attention U-Net
  • CLAHE
  • panoramic radiography

CCDS Authors

Shakib Absar
Shakib Absar

Shakib Absar

  • Intern, CCDS

Bachelors

Leading University, Sylhet

Iftekhar Ahmed
Iftekhar Ahmed

Iftekhar Ahmed

  • Intern, CCDS

Bachelors

Leading University, Sylhet

Siam Tahsin Bhuiyan
Siam Tahsin Bhuiyan

Siam Tahsin Bhuiyan

  • Intern, CCDS

Bachelors

Independent University, Bangladesh

Published at

Scientific Reports

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Md. Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md. Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

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