论文标题
点猫：管状结构提取的点集表示
PointScatter: Point Set Representation for Tubular Structure Extraction
论文作者
论文摘要
本文探讨了管状结构提取任务的点集表示。与传统的面具表示相比，点集表示具有其灵活性和表示能力，这不会受到固定网格作为掩模的限制。在此灵感的启发下，我们提出了PointCatter，这是管状结构提取任务的分割模型的替代方法。 PointCatter将图像拆分为散点区域，并且可行预测每个散点区域的点。我们进一步提出了基于贪婪的区域匹配算法的基于贪婪的区域，以端到端训练网络。我们在四个公共管状数据集上基准测试了点刻表，并且有关管状结构分割和中心线提取任务的广泛实验证明了我们方法的有效性。代码可在https://github.com/zhangzhao2022/pointscatter上找到。
This paper explores the point set representation for tubular structure extraction tasks. Compared with the traditional mask representation, the point set representation enjoys its flexibility and representation ability, which would not be restricted by the fixed grid as the mask. Inspired by this, we propose PointScatter, an alternative to the segmentation models for the tubular structure extraction task. PointScatter splits the image into scatter regions and parallelly predicts points for each scatter region. We further propose the greedy-based region-wise bipartite matching algorithm to train the network end-to-end and efficiently. We benchmark the PointScatter on four public tubular datasets, and the extensive experiments on tubular structure segmentation and centerline extraction task demonstrate the effectiveness of our approach. Code is available at https://github.com/zhangzhao2022/pointscatter.
