  Image segmentation is widely used in a variety of computer vision tasks, such
as object localization and recognition, boundary detection, and medical
imaging. This thesis proposes deep learning architectures to improve automatic
object localization and boundary delineation for salient object segmentation in
natural images and for 2D medical image segmentation. First, we propose and
evaluate a novel dilated dense encoder-decoder architecture with a custom
dilated spatial pyramid pooling block to accurately localize and delineate
boundaries for salient object segmentation. The dilation offers better spatial
understanding and the dense connectivity preserves features learned at
shallower levels of the network for better localization. Tested on three
publicly available datasets, our architecture outperforms the state-of-the-art
for one and is very competitive on the other two. Second, we propose and
evaluate a custom 2D dilated dense UNet architecture for accurate lesion
localization and segmentation in medical images. This architecture can be
utilized as a stand-alone segmentation framework or used as a rich feature
extracting backbone to aid other models in medical image segmentation. Our
architecture outperforms all baseline models for accurate lesion localization
and segmentation on a new dataset. We furthermore explore the main
considerations that should be taken into account for 3D medical image
segmentation, among them preprocessing techniques and specialized loss
functions.
