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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Wang, G; Zuluaga, MA; Li, W; Pratt, R; Patel, PA; Aertsen, M; Doel, T; ... Vercauteren, T; + view all (2019) DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence , 41 (7) 1559 -1572. 10.1109/TPAMI.2018.2840695. Green open access

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Abstract

Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.

Type: Article
Title: DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TPAMI.2018.2840695
Publisher version: https://doi.org/10.1109/TPAMI.2018.2840695
Language: English
Additional information: Copyright © 2018. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Keywords: Interactive image segmentation, convolutional neural network, geodesic distance, conditional random fields
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Maternal and Fetal Medicine
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1562775
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