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Slic-Seg: Slice-by-slice Segmentation Propagation of the Placenta in Fetal MRI using One-plane Scribbles and Online Learning

Wang, G; Zuluaga, MA; Pratt, R; Aertsen, M; David, AL; Deprest, J; Vercauteren, T; (2015) Slic-Seg: Slice-by-slice Segmentation Propagation of the Placenta in Fetal MRI using One-plane Scribbles and Online Learning. In: Navab, N and Hornegger, J and Wells, WM and Frangi, AF, (eds.) Proceeding of Medical Image Computing and Computer-Assisted Intervention — MICCAI 2015. (pp. pp. 29-37). Springer International Publishing: Munich, Germany. Green open access

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Abstract

Segmentation of the placenta from fetal MRI is critical for planning of fetal surgical procedures. Unfortunately, it is made difficult by poor image quality due to sparse acquisition, inter-slice motion, and the widely varying position and orientation of the placenta between pregnant women. We propose a minimally interactive online learning-based method named Slic-Seg to obtain accurate placenta segmentations from MRI. An online random forest is first trained on data coming from scribbles provided by the user in one single selected start slice. This then forms the basis for a slice-by-slice framework that segments subsequent slices before incorporating them into the training set on the fly. The proposed method was compared with its offline counterpart that is with no retraining, and with two other widely used interactive methods. Experiments show that our method 1) has a high performance in the start slice even in cases where sparse scribbles provided by the user lead to poor results with the competitive approaches, 2) has a robust segmentation in subsequent slices, and 3) results in less variability between users.

Type: Proceedings paper
Title: Slic-Seg: Slice-by-slice Segmentation Propagation of the Placenta in Fetal MRI using One-plane Scribbles and Online Learning
Event: MICCAI 2015, the 18th International Conference on Medical Image Computing and Computer Assisted Intervention, Munich, Germany,
Location: Munich, Germany.
Dates: 05 October 2015 - 09 October 2015
ISBN: 9783319245737
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-24574-4_4
Publisher version: http://dx.doi.org/10.1007/978-3-319-24574-4_4
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Augmented reality, biomechanical simulation, computational anatomy, image analysis, robot intervention, brain network analysis, classification, clustering, computational geometry, computer aided diagnosis, deep neural networks, intervention planning, medical imaging, navigation, normalization, statistical atlases, support vector machines, surgical simulation, visualization, x-ray imaging.
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/1468799
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