DefSynUS: Real-time Patient-specific Intrahepatic Vessel Identification via Deformation-Aware CT-US Domain Adaptation

2026-05-28

AI Summary

This study addresses the significant challenge of real-time intrahepatic vessel identification during laparoscopic liver surgery, where limited ultrasound probe access, complex vascular anatomy, and intraoperative tissue deformation hinder accurate visualization. The authors propose a novel method that eliminates the need for preoperative ultrasound by leveraging preoperative CT-based vascular annotations to generate deformation-aware synthetic ultrasound images via differentiable physics-based rendering. Integrated with cross-modal domain adaptation and deep learning–based vessel segmentation, this approach enables end-to-end, patient-specific, real-time vessel recognition. To the best of the authors’ knowledge, it is the first framework to jointly optimize deformation-aware differentiable ultrasound rendering and domain adaptation, demonstrating robust and real-time identification of intrahepatic vessel branches under novel patient poses in both phantom and preliminary clinical experiments.

Abstract

Purpose: Laparoscopic ultrasound (LUS) enhances the safety of liver surgery by visualizing intrahepatic vessels in real-time. Still, vessel identification remains difficult due to probe constraints, complex vascular structure, and tissue deformation. This work aims to enable real-time, patient-specific vessel identification that remains robust under deformation through deformable ultrasound augmentation. Methods: Preoperative CT vessel annotations are used to generate synthetic ultrasound data via optimized physics-based rendering, coupled with domain adaptation to intraoperative ultrasound. The rendering is trained end-to-end for vessel identification and patient-specificity, eliminating the need for preoperative ultrasound. A deformation-aware augmentation simulates realistic intraoperative motion and tissue deformation within the rendering pipeline. Results: In abdominal phantom and limited clinical feasibility experiments (single-case clinical evaluation), the framework achieved real-time intrahepatic vessel-branch identification, maintaining performance under new patient poses. Conclusion: The framework enables real-time vessel identification without preoperative ultrasound and supports technical feasibility, but multi-patient validation is still needed for generalizability and clinical feasibility.

Problem

• intrahepatic vessel identification
• tissue deformation
• laparoscopic ultrasound
• patient-specific
• real-time

Innovation

• deformation-aware augmentation
• synthetic ultrasound
• domain adaptation
• patient-specific vessel identification
• real-time intraoperative imaging