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

Authors

Muhammad Awais
Mais Al Taie
Caleb S O'Connor
Austin H Castelo
Belkacem Acidi
Hop S Tran Cao
Kristy K Brock

Publication Date

10-30-2024

Journal

Cancers

Abstract

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.

Keywords

liver vessel segmentation, deep learning, intraoperative ultrasound (IOUS) video frames, 2D-weighted U-Net model

DOI

10.3390/cancers16213674

PMID

39518111

PMCID

PMC11545685

PubMedCentral® Posted Date

10-30-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes