arXiv 2605.247872026-05-24eess.IV

SurgRFO：基于基础模型的术中胸片中关键残留异物组合合成

SurgRFO: Foundation Model Based Compositional Synthesis of Critical Retained Foreign Objects in Intraoperative Chest X-rays

Yuanyun Hu, Yuli Wang, Noemi Acevedo Rodriguez, Ronald Yang, Wen-Chi Hsu, Siwei Luo, Zihao Bai, Jing Wu, Yuwei Dai, Shaoju Wu, Jonathon Lindquist, Justin Honce,… 展开作者

Yuanyun Hu, Yuli Wang, Noemi Acevedo Rodriguez, Ronald Yang, Wen-Chi Hsu, Siwei Luo, Zihao Bai, Jing Wu, Yuwei Dai, Shaoju Wu, Jonathon Lindquist, Justin Honce, Premal Trivedi, Zhicheng Jiao, Ihab Kamel, Elliott Haut, Pamela Johnson, John Eng, Cheng Ting Lin, Nan Su, Bo Chen, Sun Yu, Harrison Bai

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AI总结：

提出SurgRFO两阶段合成框架，利用X光基础模型和条件泊松融合生成逼真的术中胸片残留异物图像，提升检测模型在低假阳性率下的灵敏度。

摘要：

术中胸片上的关键残留异物（RFO）是罕见但高风险的事件。其稀缺性限制了稳健的自动检测模型训练和泛化。我们引入SurgRFO，一个两阶段合成框架，用于生成逼真的存在RFO的术中胸片。在第一阶段，一个Roentgen胸部X光基础模型在手术域图像上进行微调，以生成保留解剖结构、留置管路和术中成像特征的逼真无RFO背景。在第二阶段，一个在有限阳性病例的局部RFO补丁上训练的轻量级生成器合成多样的RFO实例，并通过条件泊松融合将其合成到生成的背景上，以改善光度一致性。我们通过（i）评估真实性和临床合理性的盲法临床医生研究，以及（ii）使用合成数据增强Faster R-CNN、YOLOv8和RetinaNet的下游检测实验来评估SurgRFO。在内部和外部测试集上，SurgRFO在低假阳性率（FPPI）操作点持续提高灵敏度。临床医生评分表明，合成图像达到了与真实术中图像相当的真实性。消融分析进一步研究了融合策略和合成规模。还讨论了合成手术数据的伦理保障。

英文摘要：

Critical retained foreign objects (RFOs) on intraoperative chest radiographs are rare but high-risk events. Their scarcity limits robust automated detection model training and generalization. We introduce SurgRFO, a two-stage synthesis framework for generating realistic RFO-present intraoperative chest X-rays. In Stage 1, a Roentgen chest X-ray foundation model is fine-tuned on surgical-domain images to generate realistic RFO-free backgrounds that preserve anatomy, indwelling lines and tubes, and intraoperative imaging characteristics. In Stage 2, a lightweight generator trained on localized RFO patches from limited positive cases synthesizes diverse RFO instances, which are composited onto generated backgrounds using conditional Poisson fusion to improve photometric consistency. We evaluate SurgRFO through (i) a blinded clinician study assessing realism and clinical plausibility, and (ii) downstream detection experiments in which synthesized data are used to augment Faster R-CNN, YOLOv8, and RetinaNet. SurgRFO consistently improves sensitivity at low false-positive-per-image (FPPI) operating points on internal and external test sets. Clinician ratings indicate that the synthesized images achieve realism comparable to real intraoperative images. Ablation analyses further examine fusion strategies and synthesis scale. Ethical safeguards for synthetic surgical data are also discussed.

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