用生成模型自动生成带标注的手术出血图像，解决数据少且难采集的问题。
orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
- 基于风格迁移与关系位置学习生成逼真出血图像
- 合成数据集使检测准确率达90%，帧级最高达99%
- 适合医疗AI研究者快速构建高质量标注数据
医学影像深度学习面临数据多样性不足、伦理问题、采集成本高及精确标注难等挑战。手术中出血检测与定位尤为困难，因缺乏真实场景下的高质量数据集。本文提出orGAN，一种基于GAN的系统，可生成高保真、带标注的手术出血图像。该方法利用小型“模拟器官”数据集，构建能复现组织特性与出血过程的合成模型，降低伦理风险与数据收集成本。orGAN在StyleGAN基础上引入关系位置学习，真实模拟出血事件并标记坐标；再通过LaMa修复模块还原无出血状态，实现像素级精准标注。评估显示，结合orGAN与模拟器官数据的平衡数据集，在手术场景下检测准确率达90%，帧级最高达99%。尽管训练数据缺乏多样器官形态且含术中伪影，orGAN仍显著推动了伦理、高效、低成本生成真实标注出血数据集，助力AI更广泛融入手术实践。
原文摘要 · Abstract (English)
Deep learning in medical imaging faces obstacles: limited data diversity, ethical issues, high acquisition costs, and the need for precise annotations. Bleeding detection and localization during surgery is especially challenging due to the scarcity of high-quality datasets that reflect real surgical scenarios. We propose orGAN, a GAN-based system for generating high-fidelity, annotated surgical images of bleeding. By leveraging small "mimicking organ" datasets, synthetic models that replicate tissue properties and bleeding, our approach reduces ethical concerns and data-collection costs. orGAN builds on StyleGAN with Relational Positional Learning to simulate bleeding events realistically and mark bleeding coordinates. A LaMa-based inpainting module then restores clean, pre-bleed visuals, enabling precise pixel-level annotations. In evaluations, a balanced dataset of orGAN and mimicking-organ images achieved 90% detection accuracy in surgical settings and up to 99% frame-level accuracy. While our development data lack diverse organ morphologies and contain intraoperative artifacts, orGAN markedly advances ethical, efficient, and cost-effective creation of realistic annotated bleeding datasets, supporting broader integration of AI in surgical practice.
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