HRVVS：一种基于分层自回归残差先验的高分辨率视频血管分割网络
HRVVS: A High-resolution Video Vasculature Segmentation Network via Hierarchical Autoregressive Residual Priors
AI总结：
针对手术视频肝脏血管分割研究缺乏数据集、任务复杂的问题，构建含35段视频、11442帧的标注数据集，提出嵌入预训练VAR先验、带动态内存解码器的HRVVS网络，性能超越现有最优方法。
摘要：
肝切除手术场景下，手术视频中的肝脏血管分割具有重要的临床意义。然而，由于缺乏合适的数据集，且任务本身具有复杂的固有特性，该领域的相关研究报道较少。为解决这一问题，我们首先构建了一个高质量的逐帧标注肝脏血管数据集，该数据集包含35段长时肝切除手术视频和11442帧高分辨率画面。在此基础上，我们提出了一种新型高分辨率视频血管分割网络，命名为HRVVS。我们创新性地将预训练的视觉自回归建模（VAR）模型作为先验信息嵌入分层编码器的不同层中，以减少下采样过程中产生的信息退化。此外，我们在多视图分割网络上设计了动态内存解码器，在保留帧间更多细节的同时，最大程度减少冗余信息的传输。在手术视频数据集上开展的大量实验表明，我们提出的HRVVS性能显著优于现有最优方法。源代码和数据集将公开发布于{https://github.com/scott-yjyang/HRVVS}。
英文摘要：
The segmentation of the hepatic vasculature in surgical videos holds substantial clinical significance in the context of hepatectomy procedures. However, owing to the dearth of an appropriate dataset and the inherently complex task characteristics, few researches have been reported in this domain. To address this issue, we first introduce a high quality frame-by-frame annotated hepatic vasculature dataset containing 35 long hepatectomy videos and 11442 high-resolution frames. On this basis, we propose a novel high-resolution video vasculature segmentation network, dubbed as HRVVS. We innovatively embed a pretrained visual autoregressive modeling (VAR) model into different layers of the hierarchical encoder as prior information to reduce the information degradation generated during the downsampling process. In addition, we designed a dynamic memory decoder on a multi-view segmentation network to minimize the transmission of redundant information while preserving more details between frames. Extensive experiments on surgical video datasets demonstrate that our proposed HRVVS significantly outperforms the state-of-the-art methods. The source code and dataset will be publicly available at \{https://github.com/scott-yjyang/HRVVS}.
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。
