Abstract
Blood-vessel segmentation in cerebral angiograms is a valuable tool for medical diagnosis. However, manual blood-vessel segmentation is a time consuming process that requires high levels of expertise. The automatic detection of blood vessels can not only improve efficiency but also allow for the development of automatic diagnosis systems. Vessel detection can be approached as a binary classification problem, identifying each pixel as a vessel or non-vessel. In this paper, we use deep convolutional neural networks (CNNs) for vessel segmentation. The network is tested on a cerebral angiogram dataset. The results show the effectiveness of deep learning approach resulting in an accuracy of 95%.
Yang Fu and Jiawen Fang contributed equally to this paper.
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Acknowledgments
Prof. Scalzo was partially supported by a AHA grant 16BGIA27760152, a Spitzer grant, and received hardware donations from Gigabyte, Nvidia, and Intel.
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Fu, Y., Fang, J., Quachtran, B., Chachkhiani, N., Scalzo, F. (2016). Vessel Detection on Cerebral Angiograms Using Convolutional Neural Networks. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_59
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DOI: https://doi.org/10.1007/978-3-319-50835-1_59
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