Generative data augmentation for biliary tract detection on intraoperative images
This work addresses the critical need to improve intraoperative visualization of the bile duct to prevent injuries in laparoscopic surgery, which is an incremental advancement in medical imaging and surgical safety.
The paper tackled the problem of bile duct injury during laparoscopic cholecystectomy by developing a deep-learning approach using Yolo for biliary tract detection from intraoperative white-light images, employing classical data augmentation and a Generative Adversarial Network (GAN) to generate synthetic training data.
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.