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.
C. F. Baumgartne and L. M. Koch contributed equally.
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https://www.creatis.insa-lyon.fr/Challenge/acdc (last accessed 26 July 2017).
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Baumgartner, C.F., Koch, L.M., Pollefeys, M., Konukoglu, E. (2018). An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation. In: , et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham. https://doi.org/10.1007/978-3-319-75541-0_12
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