Blood vessel segmentation is an important step in the automated diagnosis of ophthalmic disease from retinal fundus images. The UNet is a popular encoder-decoder architecture widely used in biomedical pixel-wise seg- mentation problems. In this paper, we analyze how the UNet can be used in a more computationally efficient way. Pre-trained weights are used to initialize the network and 3 different architectures are used to compare and analyze the efficacy of the models in terms of both computational cost and performance. Three different deep architectures (VGG16, ResNet34, DenseNet121) are discussed and their efficiencies are compared for the blood vessel segmentation task. Resnet34 architecture achieved highest sensitivity of 0.849 and accuracy and specificity of 0.961, 0.9843 with number of parameters as low as 510178 compared to normal UNet with 34525168 parameters and a sensitivity of 0.756.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.