论文标题
Ribseg V2：用于肋骨标签和解剖中心线提取的大规模基准
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
论文作者
论文摘要
自动肋骨标记和解剖中心线提取是各种临床应用的常见先决条件。先前的研究要么使用社区无法访问的内部数据集，要么专注于忽略肋骨标签的临床意义的肋骨分割。为了解决这些问题，我们将二进制肋骨分割任务的先前数据集（RIBSEG）扩展到一个名为Ribseg V2的全面基准测试，并进行了660 CT扫描（总共15,466个单独的肋骨），并由专家手动检查注释核石标记和解剖中心线提取。基于Ribseg V2，我们开发了一条管道，包括基于深度学习的肋骨标记方法，以及一种基于骨架化的中心线提取方法。为了提高计算效率，我们提出了CT扫描的稀疏点云表示，并将其与标准密集的素网格进行比较。此外，我们设计和分析评估指标，以应对每个任务的主要挑战。我们的数据集，代码和模型可在线提供，以促进https://github.com/m3dv/ribseg的开放研究
Automatic rib labeling and anatomical centerline extraction are common prerequisites for various clinical applications. Prior studies either use in-house datasets that are inaccessible to communities, or focus on rib segmentation that neglects the clinical significance of rib labeling. To address these issues, we extend our prior dataset (RibSeg) on the binary rib segmentation task to a comprehensive benchmark, named RibSeg v2, with 660 CT scans (15,466 individual ribs in total) and annotations manually inspected by experts for rib labeling and anatomical centerline extraction. Based on the RibSeg v2, we develop a pipeline including deep learning-based methods for rib labeling, and a skeletonization-based method for centerline extraction. To improve computational efficiency, we propose a sparse point cloud representation of CT scans and compare it with standard dense voxel grids. Moreover, we design and analyze evaluation metrics to address the key challenges of each task. Our dataset, code, and model are available online to facilitate open research at https://github.com/M3DV/RibSeg
