In this thesis, two new architectures based on convolutional networks are proposed for segmentation of medical images. In the first architecture, a method for detecting vessel regions in angiography images is proposed which is based on deep learning approach using convolutional neural networks (CNN). The intended angiogram is first processed to enhance the image quality. Then a patch around each pixel is fed into a trained CNN to determine whether the pixel is of vessel or background regions. Experiments performed on angiograms of a dataset show that the proposed algorithm has a Dice score of 81.51 and an accuracy of 97.93. In second architecture, a new align=left Key Words Vessel segmentation, skin segmentation, deep neural networks, dense pooling layer.
