Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
RETINAL VESSEL SEGMENTATION IN MEDICAL IMAGES USING DEEP LEARNING
Download
Hazrat_Bilal_MSc_Thesis.pdf
Date
2023-8
Author
Bilal, Hazrat
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
234
views
0
downloads
Cite This
In a variety of medical applications, such as diagnostics, surgery planning, and treatment plans, medical picture segmentation makes it easier to employ innovative computational methods. Particularly, the morphology of retinal vessels is intricately linked to various ophthalmic conditions, making their precise segmentation crucial for effective medical interventions. Even though deep learning approaches have significantly improved the segmentation of retinal vessels, some difficult problems still exist and demand additional study and development. Recently, there have been notable advancements in medical image segmentation techniques utilizing U-Net and KiU-Net, showcasing promising results. The U-Net architecture falls into the category of undercomplete autoencoders, which neglects the semantic characteristics of thin and low-contrast vessels. Conversely, the KiU-Net incorporates both undercomplete and overcomplete architectures, enabling more accurate segmentation of small structures and intricate edges compared to the U-Net. Nevertheless, this approach still lacks the desired level of accuracy and poses significant computational complexity. We introduce a novel Optimized KiU-Net model aimed at enhancing the precision of segmenting thin and low-contrast retinal blood vessels, while concurrently reducing the computational complexity. The proposed model was developed by improving the convolution channel selection and increasing the depth of the encoder. Our model achieves faster convergence by utilizing a smaller parameter count and employing feature map concatenation at the final layer instead of connecting them at each block. On the RITE dataset, we run tests to verify the viability of our proposed method. The outcomes show that our optimized KiU-Net outperforms other cutting-edge techniques. Best to our knowledge, the proposed model outperforms all existing methods used for retinal vessel segmentation, with an excellent F1 score of 79.80 and an IoU of 66.30. Additionally, the Optimized KiU-Net model’s performance was assessed in contrast to cutting-edge methods using the GlaS dataset. The proposed method outperformed these methods, attaining an F1 score of 82.21 and an IoU of 71.03.
Subject Keywords
Retinal Vessel Segmentation
,
Optimized KiU-Net
,
Deep learning
,
U-Net
,
Low Contrast
URI
https://hdl.handle.net/11511/105496
Collections
Northern Cyprus Campus, Thesis
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
H. Bilal, “RETINAL VESSEL SEGMENTATION IN MEDICAL IMAGES USING DEEP LEARNING,” M.S. - Master of Science, Middle East Technical University, 2023.