提出高分辨率肝血管分割网络，提升手术视频中血管细节识别精度
HRVVS: A High-resolution Video Vasculature Segmentation Network via Hierarchical Autoregressive Residual Priors
- 用分层自回归先验缓解下采样导致的信息损失
- 在35段肝切除视频上达到优于当前最佳的方法
- 适合医学图像分割与外科手术辅助系统研究者
肝切除术中肝血管的分割具有重要临床意义，但因缺乏合适数据集及任务本身复杂，相关研究较少。为此，我们首次构建了一个高质量的逐帧标注肝血管数据集，包含35段长时序肝切除视频和11442张高分辨率帧。在此基础上，提出一种新型高分辨率视频肝血管分割网络HRVVS。通过将预训练视觉自回归模型（VAR）嵌入分层编码器各层作为先验信息，有效缓解了下采样过程中的信息退化。同时设计动态记忆解码器，在多视图分割网络中减少冗余信息传递，保留帧间细节。在多个手术视频数据集上的大量实验表明，所提HRVVS显著优于现有先进方法。源代码与数据集将公开于https://github.com/scott-yjyang/HRVVS。
原文摘要 · Abstract (English)
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}.
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