The image segmentation task divides an image into regions of similar pixels
based on brightness, color, and texture, in which every pixel in the image is as-
signed to a label. Segmentation is vital in numerous medical imaging applications,
such as quantifying the size of tissues, the localization of diseases, treatment plan-
ning, and surgery guidance. This thesis focuses on two medical image segmentation
tasks: retinal vessel segmentation in fundus images and brain segmentation in 3D
MRI images. Finally, we introduce LEON, a lightweight neural network for edge
detection.
The first part of this thesis proposes a lightweight neural network for retinal
blood vessel segmentation. Our model achieves cutting-edge outcomes with fewer
parameters. We obtained the most outstanding performance results on CHASEDB1
and DRIVE datasets with an F1 measure of 0.8351 and 0.8242, respectively. Our
model has few parameters (0.34 million) compared to other networks such as ladder
net with 1.5 million parameters and DCU-net with 1 million parameters.
The second part of this thesis investigates the association between whole and re-
gional volumetric alterations with increasing age in a large group of healthy subjects
(n=6739, age range: 30–80). We used a deep learning model for brain segmentation
for volumetric analysis to extract quantified whole and regional brain volumes in 95
classes.
Segmentation methods are called edge or boundary-based methods based on
finding abrupt changes and discontinuities in the intensity value. The third part
of the thesis introduces a new Lightweight Edge Detection Network (LEON). The
proposed approach is designed to integrate the advantages of the deformable unit
and DepthWise Separable convolutions architecture to create a lightweight back-
bone employed for efficient feature extraction. Our experiments on BSDS500 and
NYUDv2 show that LEON, while requiring only 500000 parameters, outperforms
the current lightweight edge detectors without using pre-trained weights. / Graduate / 2022-10-12
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/14272 |
| Date | 27 September 2022 |
| Creators | Akbari, Nasrin |
| Contributors | Baniasadi, Amirali, Numanagić, Ibrahim |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
Page generated in 0.0018 seconds