Enhancing Surgical Guidance: Deep Learning-Based Liver Vessel Segmentation in Real-Time Ultrasound Video Frames.

Autor: Awais M; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Al Taie M; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., O'Connor CS; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Castelo AH; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Acidi B; Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Tran Cao HS; Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Brock KK; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.; Department the of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jazyk: angličtina
Zdroj: Cancers [Cancers (Basel)] 2024 Oct 30; Vol. 16 (21). Date of Electronic Publication: 2024 Oct 30.
DOI: 10.3390/cancers16213674
Abstrakt: Background/objectives: In the field of surgical medicine, the planning and execution of liver resection procedures present formidable challenges, primarily attributable to the intricate and highly individualized nature of liver vascular anatomy. In the current surgical milieu, intraoperative ultrasonography (IOUS) has become indispensable; however, traditional 2D ultrasound imaging's interpretability is hindered by noise and speckle artifacts. Accurate identification of critical structures for preservation during hepatectomy requires advanced surgical skills.
Methods: An AI-based model that can help detect and recognize vessels including the inferior vena cava (IVC); the right (RHV), middle (MHV), and left (LVH) hepatic veins; the portal vein (PV) and its major first and second order branches the left portal vein (LPV), right portal vein (RPV), and right anterior (RAPV) and posterior (RPPV) portal veins, for real-time IOUS navigation can be of immense value in liver surgery. This research aims to advance the capabilities of IOUS-guided interventions by applying an innovative AI-based approach named the "2D-weigthed U-Net model" for the segmentation of multiple blood vessels in real-time IOUS video frames.
Results: Our proposed deep learning (DL) model achieved a mean Dice score of 0.92 for IVC, 0.90 for RHV, 0.89 for MHV, 0.86 for LHV, 0.95 for PV, 0.93 for LPV, 0.84 for RPV, 0.85 for RAPV, and 0.96 for RPPV.
Conclusion: In the future, this research will be extended for real-time multi-label segmentation of extended vasculature in the liver, followed by the translation of our model into the surgical suite.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje