SAM-HQ + LoRA for Individual Tooth Segmentation
Overview
This repository provides LoRA adapter weights for adapting Segment Anything Model, specifically its high-quality variant SAM-HQ, to individual tooth segmentation on dental panoramic X-ray images.
The goal of this work is to improve segmentation performance under significant domain shift (medical X-ray vs. natural images) using parameter-efficient fine-tuning, without modifying the full backbone.
This repository focuses on model artifacts only. Training code and datasets are not released.
Motivation
While SAM / SAM-HQ shows strong zero-shot performance on natural images, its performance degrades on dental panoramic X-rays due to:
- subtle and ambiguous object boundaries,
- high visual similarity between adjacent teeth,
- fine-grained anatomical structures.
Full fine-tuning of SAM-HQ is impractical due to model size. LoRA enables efficient domain adaptation by training a small number of additional parameters while keeping the backbone frozen.
What This Repository Provides
- LoRA adapter weights only
- Four LoRA variants corresponding to different adaptation scopes
- No training code or dataset
LoRA Adapter Variants
This repository contains four independent LoRA adapters, each trained with a different application scope:
- Encoder + Decoder LoRA (best-performing)
- Decoder-only LoRA
- HQ-module-only LoRA
- Additional ablation variant
Each adapter can be loaded independently on top of the same SAM-HQ base model, allowing controlled comparison of adaptation strategies.
Training Strategy (Summary)
- Task formulation: individual tooth-level segmentation
(each tooth treated as an independent sample) - Prompting strategy:
- iterative prompt refinement following SAM (11 iterations)
- error-driven prompt sampling from false positive / false negative regions
- Early stopping:
- Dice ≥ 0.85 or IoU ≥ 0.85
Implementation details of the training system are documented externally.
Dataset
- Training set: 2,392 images
- Test set: 269 images
- Bad-case set: 10 images
The dataset is not publicly released due to privacy and licensing constraints.
Results
- SAM-HQ baseline Dice: 0.748
- SAM-HQ + LoRA Dice: 0.914
The best performance is achieved with the Encoder + Decoder LoRA variant. Iterative prompting significantly improves boundary precision in challenging cases.
Repository Scope & Reproducibility
- This repository provides model artifacts only
- Training code, configurations, and datasets are not released
- Detailed system design and experimental analysis are available at:
👉 https://choihyun-1110.github.io/projects/sam-finetuning.html
Intended Use & Limitations
- Intended for research and experimental use only
- Not validated for clinical diagnosis
- Performance may not generalize outside dental panoramic X-ray images
License
- Base model license follows the original SAM / SAM-HQ license
- This repository is intended for non-commercial research use
Authors
- Hyun Choi — Joint First Author
- Y. Uhm — Joint First Author
Citation
@misc{choi2025sam_hq_lora_tooth,
author = {Choi, Hyun and Uhm, Y.},
title = {SAM-HQ with LoRA for Individual Tooth Segmentation},
year = {2025},
url = {https://huggingface.co/choihyun-1110/sam-hq-lora-tooth}
}