论文标题
人类Treelike管状结构细分：全面的评论和未来观点
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives
论文作者
论文摘要
人类生理学中的各种结构遵循特异性形态，通常在非常细的尺度上表达复杂性。这种结构的例子是胸前气道，视网膜血管和肝血管。可以观察到可以观察到可以观察到可以观察到可以观察到空间布置的磁共振成像（MRI），计算机断层扫描（CT），光学相干断层扫描（OCT）等医学成像模态（MRI），计算机断层扫描（CT），可以观察到空间布置的大量2D和3D图像的集合。由于对结构的分析提供了疾病诊断，治疗计划和预后的见解，因此在医学成像中对这些结构进行分割非常重要。放射科医生手动标记广泛的数据通常是耗时且容易出错的。结果，在过去的二十年中，自动化或半自动化的计算模型已成为一个流行的医学成像研究领域，迄今为止，许多计算模型已经开发出来。在这项调查中，我们旨在对当前公开可用的数据集，细分算法和评估指标进行全面审查。此外，讨论了当前的挑战和未来的研究方向。
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.
