Retinal Vessel Segmentation Using Deep Neural Networks (CROSBI ID 623864)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Melinščak, Martina ; Prentašić, Pavle ; Lončarić, Sven
engleski
Retinal Vessel Segmentation Using Deep Neural Networks
Automatic segmentation of blood vessels in fundus images is of great importance as eye diseases as well as some systemic diseases cause observable pathologic modifications. It is a binary classification problem: for each pixel we consider two possible classes (vessel or non-vessel). We use a GPU implementation of deep max-pooling convolutional neural networks to segment blood vessels. We test our method on publicly-available DRIVE dataset and our results demonstrate the high effectiveness of the deep learning approach. Our method achieves an average accuracy and AUC of 0.9466 and 0.9749, respectively.
Blood vessel segmentation ; retinal imaging ; deep neural networks ; GPU
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Podaci o prilogu
577-582.
2015.
objavljeno
Podaci o matičnoj publikaciji
José Braz, Sebastiano Battiato and Francisco Imai
978-989-758-089-5
Podaci o skupu
10th International Conference on Computer Vision Theory and Applications (VISAPP 2015)
poster
11.03.2015-14.03.2015
Berlin, Njemačka