Abstract:
Image segmentation is introduced as partitioning an image into meaningful
and disjointed regions offering a simplified representation of the image. It is a
frontline domain of computer vision and one of the earliest problem statements
considered by researchers. Despite the considerable number of available research,
image segmentation remains a challenging endeavor in computer vision due to its
significant technical challenges.
The complex nature of this operation has made its implementations dependent
on the quality and quantity of labeled data. The imperfection of the dataset especially
in biomedical imaging would lead to the misinterpretation of such images during
diagnosis.
The purpose of this thesis is to make evident the deterioration of the
performance of U-Net in segmenting biomedical images while using non-expert
labeled datasets. Along with it, is to observe the behavior of U-Net while making
certain adjustments in the datasets used and the implementation provided. Tested in
three datasets, U-Net architecture behaves differently on datasets with different
levels of label noise. Results from the conducted experiments have been examined
from both qualitative and quantitative perspective. Nonetheless, it is worth
mentioning that there exist alterations that can be applied to the dataset images prior
to training phase that would contribute to a substantial improvement. However, such improvements are not sufficing, upholding so the fact that only experts’ annotations
would result always in satisfactory and promising results.
In addition, this thesis gives the reader a comprehensive view of the
elevations of deep learning-based techniques in computer vision and in more details
in medical image segmentation.
