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.

自动血管分割在生物医学成像中至关重要，但由于血管结构的复杂性、患者间解剖变异、公开数据集的稀缺性和图像质量等原因，精确分割仍然具有困难。本研究通过深入文献研究，探讨机器学习技术在不同器官中的应用，旨在为新型成像模态下的血管分割提供坚实的基础并确定一个稳健的基准模型。研究使用由 Hierarchical Phase Contrast Tomography (HiP CT) 在人体器官图谱项目中成像获得的三个肾脏的双注释器验证的训练数据集，并采用 nnU Net 模型进行实验评估。结果显示，尽管分割性能表现较好（clDice 值在 0.82 到 0.88 之间），但存在一些错误，如由于缺乏静水压力（HiP CT 是一种离体技术）而导致的大血管分割效果较差，以及细小血管连接性减弱和血管边界处更高的分割误差。通过本研究和输出，我们旨在为后续基于不同成像模态的模型评估建立一个基准，特别是使用 HiP CT 成像数据库。