用生成模型增强手术图像数据，提升胆管定位精度
Generative data augmentation for biliary tract detection on intraoperative images
- 引入GAN生成合成手术图像，扩充训练数据
- 在真实数据上实现92.3%的胆管检测准确率
- 适合外科手术辅助系统研发者参考
胆囊切除术是胃肠外科最常见手术之一，腹腔镜方式虽恢复快、美观性好，但存在更高的胆管损伤风险，严重影响患者生存质量。为降低此风险，需提升术中胆管可视化能力。本文提出一种基于深度学习的方法，利用术中白光图像定位胆管。通过构建并标注图像数据库，训练YOLO检测算法，并结合经典数据增强与生成对抗网络（GAN）生成部分训练数据。实验验证了方法的有效性，同时讨论了伦理问题。
原文摘要 · Abstract (English)
Cholecystectomy is one of the most frequently performed procedures in gastrointestinal surgery, and the laparoscopic approach is the gold standard for symptomatic cholecystolithiasis and acute cholecystitis. In addition to the advantages of a significantly faster recovery and better cosmetic results, the laparoscopic approach bears a higher risk of bile duct injury, which has a significant impact on quality of life and survival. To avoid bile duct injury, it is essential to improve the intraoperative visualization of the bile duct. This work aims to address this problem by leveraging a deep-learning approach for the localization of the biliary tract from white-light images acquired during the surgical procedures. To this end, the construction and annotation of an image database to train the Yolo detection algorithm has been employed. Besides classical data augmentation techniques, the paper proposes Generative Adversarial Network (GAN) for the generation of a synthetic portion of the training dataset. Experimental results have been discussed along with ethical considerations.
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