  Blood vessels of the brain provide the human brain with the required
nutrients and oxygen. As a vulnerable part of the cerebral blood supply,
pathology of small vessels can cause serious problems such as Cerebral Small
Vessel Diseases (CSVD). It has also been shown that CSVD is related to
neurodegeneration, such as Alzheimer's disease. With the advancement of 7 Tesla
MRI systems, higher spatial image resolution can be achieved, enabling the
depiction of very small vessels in the brain. Non-Deep Learning-based
approaches for vessel segmentation, e.g., Frangi's vessel enhancement with
subsequent thresholding, are capable of segmenting medium to large vessels but
often fail to segment small vessels. The sensitivity of these methods to small
vessels can be increased by extensive parameter tuning or by manual
corrections, albeit making them time-consuming, laborious, and not feasible for
larger datasets. This paper proposes a deep learning architecture to
automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic
Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a
small imperfect semi-automatically segmented dataset of only 11 subjects; using
six for training, two for validation, and three for testing. The deep learning
model based on U-Net Multi-Scale Supervision was trained using the training
subset and was made equivariant to elastic deformations in a self-supervised
manner using deformation-aware learning to improve the generalisation
performance. The proposed technique was evaluated quantitatively and
qualitatively against the test set and achieved a Dice score of 80.44 $\pm$
0.83. Furthermore, the result of the proposed method was compared against a
selected manually segmented region (62.07 resultant Dice) and has shown a
considerable improvement (18.98\%) with deformation-aware learning.
