ArticleCancers2024
Enhancing Surgical Guidance: Deep Learning-Based Liver Vessel Segmentation in Real-Time Ultrasound Video Frames.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Perioperative camrelizumab plus rivoceranib in resectable hepatocellular carcinoma: translating CARES-009 outcomes into a new therapeutic paradigm.Annals of medicine and surgery (2012) · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- [Validation of artificial intelligence algorithms for the surgical practice].Chirurgie (Heidelberg, Germany) · 2025Review
- Real-time 3D US-CT fusion-based semi-automatic puncture robot system: clinical evaluation.International journal of computer assisted radiology and surgery · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
BACKGROUND/
objectivesIn 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.
methodsAn 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.
resultsOur 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.
conclusionIn 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.