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Publication Detail
Slic-Seg: A Minimally Interactive Segmentation of the Placenta from Sparse and Motion-Corrupted Fetal MRI in Multiple Views
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Wang G, Zuluaga M, Pratt R, Aertsen M, Doel T, Klusmann M, David A, Deprest J, Vercauteren T, Ourselin S
  • Publisher:
    Elsevier
  • Publication date:
    03/05/2016
  • Journal:
    Medical Image Analysis
  • Print ISSN:
    1361-8423
  • Keywords:
    Fetal MRI, Interactive Method, Co-segmentation, Graph Cuts, Random Forests
Abstract
Segmentation of the placenta from fetal MRI is challenging due to sparse acquisition, inter-slice motion, and the widely varying position and shape of the placenta between pregnant women. We propose a minimally interactive framework that combines multiple volumes acquired in different views to obtain accurate segmentation of the placenta. In the first phase, a minimally interactive slice-by-slice propagation method called Slic-Seg is used to obtain an initial segmentation from a single motion-corrupted sparse volume image. It combines high-level features, online Random Forests and Conditional Random Fields, and only needs user interactions in a single slice. In the second phase, to take advantage of the complementary resolution in multiple volumes acquired in different views, we further propose a probability-based 4D Graph Cuts method to refine the initial segmentations using inter-slice and inter-image consistency. We used our minimally interactive framework to examine the placentas of 16 mid-gestation patients from MRI acquired in axial and sagittal views respectively. The results show the proposed method has 1) a good performance even in cases where sparse scribbles provided by the user lead to poor results with the competitive propagation approaches; 2) a good interactivity with low intra- and inter-operator variability; 3) higher accuracy than state-of-the-art interactive segmentation methods; and 4) an improved accuracy due to the co-segmentation based refinement, which outperforms single volume or intensity-based Graph Cuts.
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Maternal & Fetal Medicine
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Dept of Med Phys & Biomedical Eng
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Dept of Med Phys & Biomedical Eng
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Dept of Med Phys & Biomedical Eng
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