Generative data augmentation for biliary tract detection on intraoperative images

2025-09-23

AI Summary

Laparoscopic cholecystectomy carries a high risk of bile duct injury, primarily due to inadequate intraoperative visualization of biliary anatomy. To address the scarcity of annotated real-world laparoscopic images, this work proposes a hybrid detection framework integrating Generative Adversarial Networks (GANs) with YOLOv5: an anatomy-constrained GAN synthesizes high-fidelity, anatomically plausible biliary images, while joint training with classical augmentation and synthetic data enhances model generalizability. Experimental results demonstrate a 12.6% improvement in mean Average Precision (mAP) for biliary structure detection over baseline models trained solely on real data or conventional augmentations. The key contributions are: (i) the first application of anatomy-aware GAN-based synthesis to laparoscopic biliary detection; and (ii) a systematic evaluation of the clinical utility and ethical boundaries of synthetic medical imagery for surgical decision support.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Detecting biliary tract in intraoperative images to prevent bile duct injuries
• Improving surgical visualization during laparoscopic cholecystectomy procedures
• Addressing limited training data using generative adversarial network augmentation

Innovation

• Uses Yolo deep learning for bile duct detection
• Employs Generative Adversarial Network for data augmentation
• Builds annotated image database from surgical white-light images