Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning

Tian Zhou; Ilknur Icke; Belma Dogdas; Sarayu Parimal; Smita Sampath; Joseph Forbes; Ansuman Bagchi; Chih-Liang Chin; Antong Chen

doi:10.1117/12.2253963

24 February 2017 Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning

Tian Zhou, Ilknur Icke, Belma Dogdas, Sarayu Parimal, Smita Sampath, Joseph Forbes, Ansuman Bagchi, Chih-Liang Chin, Antong Chen

Author Affiliations +

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101331W (2017) https://doi.org/10.1117/12.2253963
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

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.

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Tian Zhou, Ilknur Icke, Belma Dogdas, Sarayu Parimal, Smita Sampath, Joseph Forbes, Ansuman Bagchi, Chih-Liang Chin, and Antong Chen "Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331W (24 February 2017); https://doi.org/10.1117/12.2253963

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