@inproceedings{12ecf80a0bfb46a7b72d55203c6f2af0,
title = "Active learning based segmentation of Crohn's disease using principles of visual saliency",
abstract = "We propose a active learning (AL) approach to segment Crohn's disease (CD) affected regions in abdominal magnetic resonance (MR) images. Our label query strategy is inspired from the principles of visual saliency which has similar considerations for choosing the most salient region. These similarities are encoded in a graph using classification maps and low level features. The most informative node is determined using random walks. Experimental results on real patient datasets show the superior performance of our approach and highlight the importance of different features to determine a region's importance.",
keywords = "Active learning, Crohn disease, Random forests, Random walks, Saliency, Segmentation",
author = "Dwarikanath Mahapatra and Sch{\"u}ffler, {Peter J.} and Tielbeek, {Jeroen A.W.} and Makanyanga, {Jesica C.} and Jaap Stoker and Taylor, {Stuart A.} and Vos, {Franciscus M.} and Buhmann, {Joachim M.}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 ; Conference date: 29-04-2014 Through 02-05-2014",
year = "2014",
month = jul,
day = "29",
doi = "10.1109/isbi.2014.6867850",
language = "English",
series = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "226--229",
booktitle = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",
}