Retinal vessel segmentation using convolutional neural networks

Date

2018-05-05

Author

Ulusoy, İlkay

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

65
views

0
downloads

Retinal vessel segmentation and extracting features such as tortuosity, width, length related to those vessels can be used in diagnosis, treatment and screening of many diseases such as retinopathy of prematurity, hypertension and diabetes. Therefore, automatic segmentation of vessels by computers will make the analysis of those diseases easier and will help during the screening, diagnosis and treatment processes. In this study, a solution based on convolutional neural networks (CNN) is proposed for automatic segmentation of retinal vessels. The proposed CNN model is tested on DRIVE dataset and a better performance than literature is achieved.

Subject Keywords

Image segmentation, Biomedical imaging, Blood vessels, Retinal vessels, Convolutional neural networks

URI

https://hdl.handle.net/11511/41526

DOI

https://doi.org/10.1109/siu.2018.8404262

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Vessel segmentation in MRI using a variational image subtraction approach SARAN, AYŞE NURDAN; Nar, Fatih; SARAN, MURAT (2014-01-01) Vessel segmentation is important for many clinical applications, such as the diagnosis of vascular diseases, the planning of surgery, or the monitoring of the progress of disease. Although various approaches have been proposed to segment vessel structures from 3-dimensional medical images, to the best of our knowledge, there has been no known technique that uses magnetic resonance imaging (MRI) as prior information within the vessel segmentation of magnetic resonance angiography (MRA) or magnetic resonance ...
AUTOMATIC SEGMENTATION OF NUCLEI IN HISTOPATHOLOGY IMAGES USING ENCODING-DECODING CONVOLUTIONAL NEURAL NETWORKS Mercadier, Deniz Sayin; Beşbınar, Beril; Frossard, Pascal (2019-01-01) Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, adaptivity and generalizability. Popular Deep Convolutional Neural Networks (DCNNs) have recently been utilized for nuclei segmentation, outperforming traditional approaches that exploit color and texture features in combination with shallow classifiers o...
Examination of the dielectrophoretic spectra of MCF7 breast cancer cells and leukocytes Çağlayan, Zeynep; Demircan Yalçın, Yağmur; Külah, Haluk (Wiley, 2020-03-01) The detection of circulating tumor cells (CTCs) in blood is crucial to assess metastatic progression and to guide therapy. Dielectrophoresis (DEP) is a powerful cell surface marker-free method that allows intrinsic dielectric properties of suspended cells to be exploited for CTC enrichment/isolation from blood. Design of a successful DEP-based CTC enrichment/isolation system requires that the DEP response of the targeted particles should accurately be known. This paper presents a DEP spectrum method to inve...
Retinal pigment epithelium cell culture on surface modified poly(hydroxybutyrate-co-hydroxyvalerate) thin films Tezcaner, Ayşen; Hasırcı, Vasıf Nejat (2003-01-01) There is currently no effective treatment for the retinal disorders caused by retinal pigment epithelium (RPE) degeneration. Transplantation of allografts is the main strategy towards correction of this malady. Tissue engineering could offer hope and involve the use of biodegradable polymeric templates to replace diseased or lost RPE. In this study PHBV8 film was chosen as a temporary substrate for growing retinal pigment epithelium cells as an organized monolayer before their subretinal transplantation. Th...
Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling Şener, Emre; Mumcuoğlu, Ünal Erkan; Hamcan, Salih (2016-02-01) Background: Accurate segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification.

Citation Formats

İ. Ulusoy, “Retinal vessel segmentation using convolutional neural networks,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41526.