  Pancreatic cancer is one of the deadliest types of cancer, with 25% of the
diagnosed patients surviving for only one year and 6% of them for five.
Computed tomography (CT) screening trials have played a key role in improving
early detection of pancreatic cancer, which has shown significant improvement
in patient survival rates. However, advanced analysis of such images often
requires manual segmentation of the pancreas, which is a time-consuming task.
Moreover, pancreas presents high variability in shape, while occupying only a
very small area of the entire abdominal CT scans, which increases the
complexity of the problem. The rapid development of deep learning can
contribute to offering robust algorithms that provide inexpensive, accurate,
and user-independent segmentation results that can guide the domain experts.
This dissertation addresses this task by investigating a two-step approach for
pancreas segmentation, by assisting the task with a prior rough localization or
detection of pancreas. This rough localization of the pancreas is provided by
an estimated probability map and the detection task is achieved by using the
YOLOv4 deep learning algorithm. The segmentation task is tackled by a modified
U-Net model applied on cropped data, as well as by using a morphological active
contours algorithm. For comparison, the U-Net model was also applied on the
full CT images, which provide a coarse pancreas segmentation to serve as
reference. Experimental results of the detection network on the National
Institutes of Health (NIH) dataset and the pancreas tumour task dataset within
the Medical Segmentation Decathlon show 50.67% mean Average Precision. The best
segmentation network achieved good segmentation results on the NIH dataset,
reaching 67.67% Dice score.

Computer Vision and Pattern Recognition