  The utilisation of deep learning segmentation algorithms that learn complex
organs and tissue patterns and extract essential regions of interest from the
noisy background to improve the visual ability for medical image diagnosis has
achieved impressive results in Medical Image Computing (MIC). This thesis
focuses on retinal blood vessel segmentation tasks, providing an extensive
literature review of deep learning-based medical image segmentation approaches
while comparing the methodologies and empirical performances. The work also
examines the limitations of current state-of-the-art methods by pointing out
the two significant existing limitations: data size constraints and the
dependency on high computational resources. To address such problems, this work
proposes a novel efficient, simple multiview learning framework that
contrastively learns invariant vessel feature representation by comparing with
multiple augmented views by various transformations to overcome data shortage
and improve generalisation ability. Moreover, the hybrid network architecture
integrates the attention mechanism into a Convolutional Neural Network to
further capture complex continuous curvilinear vessel structures. The result
demonstrates the proposed method validated on the CHASE-DB1 dataset, attaining
the highest F1 score of 83.46% and the highest Intersection over Union (IOU)
score of 71.62% with UNet structure, surpassing existing benchmark UNet-based
methods by 1.95% and 2.8%, respectively. The combination of the metrics
indicates the model detects the vessel object accurately with a highly
coincidental location with the ground truth. Moreover, the proposed approach
could be trained within 30 minutes by consuming less than 3 GB GPU RAM, and
such characteristics support the efficient implementation for real-world
applications and deployments.
