Paper Info
Title
Slic-Seg: Slice-by-Slice Segmentation Propagation of the Placenta in Fetal MRI Using One-Plane Scribbles and Online Learning.
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.
Year
DOI
Venue
2015
10.1007/978-3-319-24574-4_4
Lecture Notes in Computer Science
Field
DocType
Volume
Computer vision,Computational anatomy,Pattern recognition,Medical imaging,Segmentation,Computer science,Support vector machine,Computer-aided diagnosis,Image quality,Artificial intelligence,Cluster analysis,Random forest
Conference
9351
ISSN
Citations 
PageRank 
0302-9743
6
0.58
References 
Authors
7
8
Name
Order
Citations
PageRank
Guotai Wang1877.68
Maria A. Zuluaga227925.84
Rosalind Pratt3744.07
Michael Aertsen4816.21
Anna L. David5525.70
Jan Deprest612320.45
Tom Vercauteren71956108.68
Sébastien Ourselin82499237.61