Automated blood vessel segmentation is vital for biomedical imaging, as vessel changes indicate many pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. We present a thorough literature review, highlighting the state of machine learning techniques across diverse organs. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation in a new imaging modality, Hierarchical Phase Contrast Tomography (HiP CT). Introduced in 2020 at the European Synchrotron Radiation Facility, HiP CT enables 3D imaging of complete organs at an unprecedented resolution of ca. 20mm per voxel, with the capability for localized zooms in selected regions down to 1mm per voxel without sectioning. We have created a training dataset with double annotator validated vascular data from three kidneys imaged with HiP CT in the context of the Human Organ Atlas Project. Finally, utilising the nnU Net model, we conduct experiments to assess the models performance on both familiar and unseen samples, employing vessel specific metrics. Our results show that while segmentations yielded reasonably high scores such as clDice values ranging from 0.82 to 0.88, certain errors persisted. Large vessels that collapsed due to the lack of hydrostatic pressure (HiP CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors obstruct the understanding of the structures by interrupting vascular tree connectivity. Through our review and outputs, we aim to set a benchmark for subsequent model evaluations using various modalities, especially with the HiP CT imaging database.

翻译：自动化血管分割在生物医学成像中至关重要，因为血管变化可指示多种病理状态。然而，由于血管结构的复杂性、患者间的解剖差异、公开标注数据集的稀缺性以及图像质量等因素，精确分割仍然困难重重。本文通过全面的文献综述，重点阐述了机器学习技术在不同器官中的研究现状，旨在为该领域奠定基础，并确定适用于新兴成像模态——分层相位衬度断层成像（HiP CT）血管分割的稳健基线模型。HiP CT于2020年在欧洲同步辐射装置问世，能够以每个体素约20毫米的前所未有分辨率对完整器官进行三维成像，并可在选定区域实现低至每个体素1毫米的局部放大而无需切片处理。我们基于人类器官图谱计划中三例HiP CT成像的肾脏数据，创建了经双重标注者验证的血管训练数据集。最后，利用nnU-Net模型开展实验，采用血管特异性指标评估模型在熟悉样本和未见样本上的性能。结果表明，尽管分割结果获得较高评分（如clDice值在0.82至0.88之间），但某些误差仍然存在：因缺乏静水压（HiP CT为离体技术）而塌陷的大血管分割效果较差，同时观察到微细血管连通性下降及血管边界分割误差增大。这些误差会中断血管树连通性，阻碍对结构形态的理解。通过综述与实验结果，我们旨在为后续基于多种成像模态（尤其是HiP CT成像数据库）的模型评估建立基准。