文章：

提升手术引导：基于深度学习的实时超声视频帧肝脏血管分割

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

原文发布日期：30 October 2024

DOI: 10.3390/cancers16213674

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

背景/目的：在肝脏外科手术领域，肝切除手术的规划与实施面临巨大挑战，这主要归因于肝脏血管解剖结构的高度复杂性和个体差异性。在当前的手术环境中，术中超声（IOUS）已成为不可或缺的工具；然而，传统二维超声图像因噪声和斑点伪影的影响，其可解读性受到限制。在肝切除术中准确识别需保留的关键结构，需要高超的手术技巧。方法：一种基于人工智能的模型，能够实时辅助检测和识别包括下腔静脉（IVC）、肝右静脉（RHV）、肝中静脉（MHV）、肝左静脉（LHV）、门静脉（PV）及其主要一级和二级分支（左门静脉LPV、右门静脉RPV、右前门静脉RAPV和右后门静脉RPPV）在内的血管，对于实时IOUS导航在肝脏手术中具有巨大价值。本研究旨在通过应用一种名为“2D加权U-Net模型”的创新性人工智能方法，对实时IOUS视频帧中的多类血管进行分割，从而提升IOUS引导介入手术的能力。结果：我们提出的深度学习（DL）模型获得的平均Dice分数分别为：IVC 0.92、RHV 0.90、MHV 0.89、LHV 0.86、PV 0.95、LPV 0.93、RPV 0.84、RAPV 0.85、RPPV 0.96。结论：未来，本研究将扩展至对肝脏扩展血管系统进行实时多标签分割，并最终将我们的模型转化应用于手术室。

原文链接：

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