NDLI: Experiments in Retinal Vascular Tree Segmentation using Deep Convolutional Neural Networks

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Experiments in Retinal Vascular Tree Segmentation using Deep Convolutional Neural Networks

Content Provider	Semantic Scholar
Author	Quadros, Francisca A.
Copyright Year	2019
Abstract	Artificial Intelligence has been emerging as one of the most promising and relevant current domains. Thus, its study has been increasingly developed and deepened, generating new solutions in the most diverse areas of our society. Particularly, in the area of computer-aided diagnosis, an increasing number of studies and projects have arisen, demonstrating the growing interest raised by this type of technologies. This dissertation represents, in this context, an attempt to analyze and explore the usage of Deep Convolutional Neural Networks in the segmentation of the retinal vascular tree in ophthalmic images. To achieve such goal, a model of a Convolutional Neural Network was developed and several training cases were executed with different parameters, to evaluate its behavior. In an early stage, the images obtained from online image databases, namely DRIVE and STARE, were slightly pre-processed and then patches of size 32 by 32 pixels were extracted to train the neural network. Therefore, the developed algorithm is supervised, once previous information about the central pixel of each patch was used to train the network. In the following phase, the model was implemented, having been tested different architectures (number and type of layers) using the Keras API. The procedure described so far, as well as all the experiments conducted, are part of the training process. Afterwards there was a test phase, in which the model was tested in a new unseen set of images to evaluate the performance of the algorithm. The final classifier was tested on 20 images from DRIVE, having achieved an AUC of 0.87, accuracy of 80%, sensitivity of 85% and specificity of 79%, with a segmentation time of 13 minutes, which translates into 39 seconds per image.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://estudogeral.sib.uc.pt/bitstream/10316/83005/1/Tese%20_final.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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