International Conference on Medical Image Computing and Computer-Assisted Intervention

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp 620-627 | Cite as

Automated Localization of Fetal Organs in MRI Using Random Forests with Steerable Features

  • Kevin Keraudren
  • Bernhard Kainz
  • Ozan Oktay
  • Vanessa Kyriakopoulou
  • Mary Rutherford
  • Joseph V. Hajnal
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)

Abstract

Fetal MRI is an invaluable diagnostic tool complementary to ultrasound thanks to its high contrast and resolution. Motion artifacts and the arbitrary orientation of the fetus are two main challenges of fetal MRI. In this paper, we propose a method based on Random Forests with steerable features to automatically localize the heart, lungs and liver in fetal MRI. During training, all MR images are mapped into a standard coordinate system that is defined by landmarks on the fetal anatomy and normalized for fetal age. Image features are then extracted in this coordinate system. During testing, features are computed for different orientations with a search space constrained by previously detected landmarks. The method was tested on healthy fetuses as well as fetuses with intrauterine growth restriction (IUGR) from 20 to 38 weeks of gestation. The detection rate was above 90% for all organs of healthy fetuses in the absence of motion artifacts. In the presence of motion, the detection rate was 83% for the heart, 78% for the lungs and 67% for the liver. Growth restriction did not decrease the performance of the heart detection but had an impact on the detection of the lungs and liver. The proposed method can be used to initialize subsequent processing steps such as segmentation or motion correction, as well as automatically orient the 3D volume based on the fetal anatomy to facilitate clinical examination.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kevin Keraudren
    • 1
  • Bernhard Kainz
    • 1
  • Ozan Oktay
    • 1
  • Vanessa Kyriakopoulou
    • 2
  • Mary Rutherford
    • 2
  • Joseph V. Hajnal
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonLondonUK
  2. 2.Department Biomedical EngineeringKing’s College LondonLondonUK

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