Towards Real-time Intrahepatic Vessel Identification in Intraoperative Ultrasound-Guided Liver Surgery

• Main Authors: Beaudet, Karl-Philippe, Karargyris, Alexandros, Hadramy, Sidaty El, Cotin, Stéphane, Mazellier, Jean-Paul, Padoy, Nicolas, Verde, Juan
• Format: Journal Article
• Language: English
• Published: 04-10-2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2410.03420

MICCAI 2024, Oct 2024, Marrakech, Morocco While laparoscopic liver resection is less prone to complications and
maintains patient outcomes compared to traditional open surgery, its complexity
hinders widespread adoption due to challenges in representing the liver's
internal structure. Laparoscopic intraoperative ultrasound offers efficient,
cost-effective and radiation-free guidance. Our objective is to aid physicians
in identifying internal liver structures using laparoscopic intraoperative
ultrasound. We propose a patient-specific approach using preoperative 3D
ultrasound liver volume to train a deep learning model for real-time
identification of portal tree and branch structures. Our personalized AI model,
validated on ex vivo swine livers, achieved superior precision (0.95) and
recall (0.93) compared to surgeons, laying groundwork for precise vessel
identification in ultrasound-based liver resection. Its adaptability and
potential clinical impact promise to advance surgical interventions and improve
patient care.