Authors
Masato Takahashi, Hiraku Fuse, Koichi Hanada, Manamu Kimura, Kenji Yasue, Shin Miyakawa, Hiroki Nosaka, Norikazu Koori
Published in
Nihon Hoshasen Gijutsu Gakkai zasshi. Volume 82. Issue 11.
Abstract
To develop a click-prompt-style semi-automatic defect segmentation method using Segment Anything Model 2 (SAM2) with Low-Rank Adaptation (LoRA) for fluoroscopic images of lead aprons and to evaluate its quantitative performance and usefulness.
Twenty-four fluoroscopic images acquired from 19 lead aprons were divided into training/validation (n=14) and test datasets (n=10). Two radiological technologists independently annotated the test images and established Consensus GT masks after reviewing discrepancies. Four radiological technologists performed manual and click-prompt-based segmentation using SAM2 Original and SAM2-LoRA (r=8, 16, and 32). All methods were evaluated against the Consensus GT.
Inter-annotator agreement between the two independent GT masks was ICC(2,1)=0.982 for area, with a Dice coefficient of 0.802 and IoU of 0.674. Mean Dice coefficients against the Consensus GT were 0.708 for manual segmentation, 0.426 for SAM2 Original, and 0.752, 0.774, and 0.759 for SAM2-LoRA with r=8, 16, and 32, respectively. The r=16 setting showed the smallest mean absolute area error among the models (69.7 mm2).
Under the conditions of this small single-center study, click-prompt-based SAM2-LoRA may support measurement of candidate defect areas in fluoroscopic images of lead aprons. External validation is required before use for automated rejection decisions.
PMID:
42893866
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.
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