Abstract:
Understanding biological processes and analyzing different diseases requires
accurate segmentation of the biomedical images that researchers have available. In
diseases like cancer, it can help in developing drugs rapidly and applying proper
treatments. However, labeling all the objects and drawing contours around them in an
MRI image or CT scan requires knowledge and experience. Fortunately for all of us,
artificial intelligence algorithms in computer vision have advanced a lot and many
tasks in image processing can be solved using Convolutional Neural Networks.
In this thesis, we designed a deep and powerful architecture, U^3-Net, for
biomedical image segmentation. The architecture of our U^3-Net network is a three-
level nested U-Net-like shape. Our proposed architecture can be implemented from
scratch without using image classification backbones. Nuclei segmentation and finding
cell confluence were the successful tasks that we solved using U^3-Net.
Finally, we measured the evaluation metrics of our proposed model and
compared it to other state-of-art architectures widely used in medical image
segmentation. In most of the datasets that we evaluated, U^3-Net achieved the highest
metrics.
