ArXiv

TLNM: Externally Validated Tooth Detection, Numbering and Segmentation from Smartphone Photographs Using Mask R-CNN

2026-08-06 · 17:03 UTC ·Arash Nedaei, Henna Tiensuu, Elina Väyrynen... ·1 min read
Authors
Arash Nedaei, Henna Tiensuu, Elina Väyrynen...
Categories
cs.CV, eess.IV
arXiv
https://arxiv.org/abs/2608.06275v1
PDF
https://arxiv.org/pdf/2608.06275v1

Brief

The paper introduces TLNM, a Mask R-CNN pipeline for tooth detection, numbering and segmentation from consumer smartphone photos, trained on 1,272 annotated images and augmented with a masked gray-world white‑balancer and anatomically constrained detection layer. The model achieves strong internal (AP@50 0.818, F1 0.884) and external (AP@50 0.901, F1 0.928) performance, shows low training variance, and is released as a containerised API for tele-dentistry applications.

Why it matters

The authors present a Mask R-CNN-based TLNM trained on 1,272 annotated smartphone photos, incorporating a masked gray-world white-balancing algorithm and an anatomically constrained detection layer to mitigate colour casts and suppress anatomically implausible detections.

Key details

  • Measured performance: internal test instance-mask AP@50 = 0.818, class-aware PQ = 0.780, operational F1 = 0.884; external test instance-mask AP@50 = 0.901, PQ = 0.832, operational F1 = 0.928.
  • Training stability and deployment: across ten training runs AP@50 standard deviation = 0.009 on the internal test set; the inference pipeline is provided as an open-source, containerised API.
Source evidence

Abstract

Oral health issues affect billions globally, but the cost and limited access to professional dental care hinder preventive oral healthcare. Research relies on clinical-grade radiographs or intraoral camera images, unavailable for public self-screening. This study introduces a tooth localisation and numbering model for smartphone photographs. We developed a customised Mask Region-based Convolutional Neural Network (Mask R-CNN) pipeline trained on 1,272 annotated smartphone images. To address variability in patient-generated health data, the pipeline incorporates two domain-informed mechanisms: a masked gray-world white-balancing algorithm to mitigate artificial colour casts and an anatomically constrained detection layer to enforce structural validity and suppress false positives. Evaluation comprised four stages: internal held-out testing, independent external testing, a descriptive ablation study, and fold-based training stability analysis using the same internal test set. On the internal test set, the model achieved an instance-mask AP@50 of 0.818, class-aware PQ of 0.780, and operational F1 of 0.884. Training stability showed limited between-model variation: across ten runs, instance-mask AP@50 had a standard deviation of 0.009. On the external dataset, the model achieved an instance-mask AP@50 of 0.901, class-aware PQ of 0.832, and operational F1 of 0.928 despite differences in population, sensors, and acquisition protocols. The inference pipeline is available as an open-source, containerised API. These results demonstrate that consumer-grade smartphone imagery can support automated tooth-level anatomical mapping, offering a scalable, potentially low-cost foundation for remote screening and tele-dentistry in resource-constrained environments.

Comment: 16 pages, 7 figures, 6 tables