title: Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views.
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.
authors.affiliation: Translational Imaging Group, CMIC, University College London, London, UK. Electronic address: guotai.wang.14@ucl.ac.uk.
authors.forename: Guotai
authors.initials: G
authors.lastname: Wang
authors.affiliation: Translational Imaging Group, CMIC, University College London, London, UK.
authors.forename: Maria A
authors.initials: MA
authors.lastname: Zuluaga
authors.affiliation: Translational Imaging Group, CMIC, University College London, London, UK; Institute for Women's Health, University College London, London, UK.
authors.forename: Rosalind
authors.initials: R
authors.lastname: Pratt
authors.affiliation: Department of Radiology, University Hospitals KU Leuven, Leuven, Belgium.
authors.forename: Michael
authors.initials: M
authors.lastname: Aertsen
authors.affiliation: Translational Imaging Group, CMIC, University College London, London, UK.
authors.forename: Tom
authors.initials: T
authors.lastname: Doel
authors.affiliation: Department of Radiology, University College London Hospital, London, UK.
authors.forename: Maria
authors.initials: M
authors.lastname: Klusmann
authors.affiliation: Institute for Women's Health, University College London, London, UK.
authors.forename: Anna L
authors.initials: AL
authors.lastname: David
authors.affiliation: Department of Obstetrics, University Hospitals KU Leuven, Leuven, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Deprest
authors.affiliation: Translational Imaging Group, CMIC, University College London, London, UK.
authors.forename: Tom
authors.initials: T
authors.lastname: Vercauteren
authors.affiliation: Translational Imaging Group, CMIC, University College London, London, UK.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Ourselin
mesh_terms: D000465:Algorithms; D005260:Female; D005333:Fetus / Q000000981:diagnostic imaging*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010920:Placenta / Q000000981:diagnostic imaging*; D011247:Pregnancy; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Co-segmentation; Fetal MRI; Graph Cuts; Interactive method; Random forests