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An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation

  • Christian F. Baumgartner
  • Lisa M. Koch
  • Marc Pollefeys
  • Ender Konukoglu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)

Abstract

Accurate segmentation of the heart is an important step towards evaluating cardiac function. In this paper, we present a fully automated framework for segmentation of the left (LV) and right (RV) ventricular cavities and the myocardium (Myo) on short-axis cardiac MR images. We investigate various 2D and 3D convolutional neural network architectures for this task. Experiments were performed on the ACDC 2017 challenge training dataset comprising cardiac MR images of 100 patients, where manual reference segmentations were made available for end-diastolic (ED) and end-systolic (ES) frames. We find that processing the images in a slice-by-slice fashion using 2D networks is beneficial due to a relatively large slice thickness. However, the exact network architecture only plays a minor role. We report mean Dice coefficients of 0.950 (LV), 0.893 (RV), and 0.899 (Myo), respectively with an average evaluation time of 1.1 s per volume on a modern GPU.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Christian F. Baumgartner
    • 1
  • Lisa M. Koch
    • 2
  • Marc Pollefeys
    • 2
  • Ender Konukoglu
    • 1
  1. 1.Computer Vision LabETH ZurichZürichSwitzerland
  2. 2.Computer Vision and Geometry GroupETH ZurichZürichSwitzerland

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