PROCEEDINGS ARTICLE | February 24, 2017
Proc. SPIE. 10133, Medical Imaging 2017: Image Processing
KEYWORDS: Mathematical modeling, Data modeling, Databases, Magnetic resonance imaging, Image segmentation, Image processing, Pattern recognition, Heart, Neural networks, Mathematics, Information technology, Machine learning, Performance modeling, Cardiovascular magnetic resonance imaging
In developing treatment of cardiovascular diseases, short axis cine MRI has been used as a standard technique for
understanding the global structural and functional characteristics of the heart, e.g. ventricle dimensions, stroke volume
and ejection fraction. To conduct an accurate assessment, heart structures need to be segmented from the cine MRI
images with high precision, which could be a laborious task when performed manually. Herein a fully automatic
framework is proposed for the segmentation of the left ventricle from the slices of short axis cine MRI scans of porcine
subjects using a deep learning approach. For training the deep learning models, which generally requires a large set of
data, a public database of human cine MRI scans is used. Experiments on the 3150 cine slices of 7 porcine subjects have
shown that when comparing the automatic and manual segmentations the mean slice-wise Dice coefficient is about
0.930, the point-to-curve error is 1.07 mm, and the mean slice-wise Hausdorff distance is around 3.70 mm, which
demonstrates the accuracy and robustness of the proposed inter-species translational approach.
