GingivitisSeg：牙龈炎分割的公开数据集与对比基准

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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 this https URL.

Comments:	Preprint. Under review at Journal of Imaging Informatics in Medicine. Dataset and code: this https URL. Dataset DOI: https://doi.org/10.5281/zenodo.21098897
Subjects:	Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2611.15184 [cs.CV]
	(or arXiv:2611.15184v1 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2611.15184 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zeynep Özdemir [view email]
[v1] Wed, 11 Nov 2026 08:31:55 UTC (5,425 KB)