Segmentation of Vascular Regions in Ultrasound Images: A Deep Learning Approach
TL;DR: A pipelined network comprising of a convolutional neural network followed by unsupervised clustering is proposed to perform vessel segmentation in liver ultrasound images, motivated by the tremendous success of CNNs in object detection and localization.
Abstract: Vascular region segmentation in ultrasound images is necessary for applications like automatic registration, and surgical navigation. In this paper, a pipelined network comprising of a convolutional neural network (CNN) followed by unsupervised clustering is proposed to perform vessel segmentation in liver ultrasound images. The work is motivated by the tremendous success of CNNs in object detection and localization. CNN here is trained to localize vascular regions, which are subsequently segmented by the clustering. The proposed network results in 99.14% pixel accuracy and 69.62% mean region intersection over union on 132 images. These values are better than some existing methods.
...connected layers of neurons , ....
"Segmentation of Vascular Regions in..." refers background in this paper
...Convolutional neural networks (CNNs) have become an ideal choice for high level vision tasks, for example, object detection , , , ....
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