  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.
