H-Net：一种用于心内导管中同时进行三维力估计和立体语义分割的多任务架构
H-Net: A Multitask Architecture for Simultaneous 3D Force Estimation and Stereo Semantic Segmentation in Intracardiac Catheters
AI总结：
本文提出轻量级多任务架构H-Net，通过双平面X射线图像同时实现导管立体语义分割与三维力估计，在两项任务上均达到最先进性能。
摘要：
导管插入手术的成功率与提供给外科医生的感知数据密切相关。基于视觉的深度学习模型能够以无传感器的方式同时提供触觉和视觉信息，并且制造成本较低。鉴于这些模型对于计算资源有限的设备而言较为复杂，现有研究分别聚焦于力估计和导管分割。然而，目前缺乏一种能够同时从两个不同角度分割导管并估计三维施加力的综合架构。为弥补这一空白，本工作提出了一种新颖的、轻量级的、多输入多输出的基于编码器-解码器的架构。该架构旨在从两个视角分割导管，并同时测量x、y和z方向上的施加力。该网络处理两幅同步的X射线图像，这些图像拟由双平面荧光透视系统提供，展示导管在不同角度下的偏转。它使用两个参数共享的并行子网络，输出与输入对应的两个分割图。此外，它利用立体视觉来估计导管尖端在三维空间中的施加力。该架构具有两个输入通道、两个用于分割的分类头和用于力估计的回归头，通过单一的端到端架构实现。所有头的输出均经过评估并与文献进行比较，在分割和力估计两方面均展现出最先进的性能。据作者所知，这是首次提出此类模型。
英文摘要：
The success rate of catheterization procedures is closely linked to the sensory data provided to the surgeon. Vision-based deep learning models can deliver both tactile and visual information in a sensor-free manner, while also being cost-effective to produce. Given the complexity of these models for devices with limited computational resources, research has focused on force estimation and catheter segmentation separately. However, there is a lack of a comprehensive architecture capable of simultaneously segmenting the catheter from two different angles and estimating the applied forces in 3D. To bridge this gap, this work proposes a novel, lightweight, multi-input, multi-output encoder-decoder-based architecture. It is designed to segment the catheter from two points of view and concurrently measure the applied forces in the x, y, and z directions. This network processes two simultaneous X-Ray images, intended to be fed by a biplane fluoroscopy system, showing a catheter's deflection from different angles. It uses two parallel sub-networks with shared parameters to output two segmentation maps corresponding to the inputs. Additionally, it leverages stereo vision to estimate the applied forces at the catheter's tip in 3D. The architecture features two input channels, two classification heads for segmentation, and a regression head for force estimation through a single end-to-end architecture. The output of all heads was assessed and compared with the literature, demonstrating state-of-the-art performance in both segmentation and force estimation. To the best of the authors' knowledge, this is the first time such a model has been proposed
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
