1 research outputs found
DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data
Blood vessels of the brain are providing 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 in 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. Deep learning model
based on U-Net Multi-Scale Supervision was trained using the training subset
and were 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 80.440.83. 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