# GingivitisSeg: A Public Dataset and Comparative Benchmark for Gingivitis Segmentation in Intraoral Images

**Authors:** Zeynep Ozdemir, Zehra Hasgul, Irem Ulku, Omer Ozgur Tanriover, Fatma Karacaoglu, Murat Akkaya  
**Published:** 11 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.15184) · [PDF](https://arxiv.org/pdf/2611.15184) · [Code](https://github.com/zeynepozdemir/GingivitisSeg) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.15184.html)

## Key numbers

- **76.49%**: of 76.49% and gingivitis IoU of 54.91%
- **54.91%**: of 76.49% and gingivitis IoU of 54.91%
- **961**: a public benchmark comprising 961 intraoral images with pixel-level annotations of gingival inflammation and predefined training

## Abstract

Gingivitis segmentation from intraoral photographs can support computer-assisted periodontal assessment, but pixel-level delineation remains challenging due to severe foreground-background class imbalance, low-contrast boundaries, and variations in illumination and anatomy. We introduce GingivitisSeg, a public benchmark comprising 961 intraoral images with pixel-level annotations of gingival inflammation and predefined training (672), validation (144), and test (145) partitions. Seven semantic segmentation model-backbone configurations were compared using standardized partitions, input resolution, and evaluation metrics. Controlled ablation experiments examined class-weighted cross-entropy loss, Dice loss, and auxiliary supervision. UPerNet with ResNet50 achieved the highest mean Intersection over Union (mIoU) of 76.49% and gingivitis IoU of 54.91%. However, differences from DeepLabV3+ with ResNet50 and SegFormer with MiT-B2 were not statistically significant after Holm correction. The evaluated optimization components improved performance when combined in the UPerNet-ResNet50 configuration. Further analyses showed that performance was associated with gingivitis region size and decreased under stronger image blur. The dataset, benchmark splits, and source code are available at https://github.com/zeynepozdemir/GingivitisSeg.

---

Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.