In the development of treatments for cardiovascular diseases, short axis cardiac cine MRI is important for the assessment of various structural and functional properties of the heart. In short axis cardiac cine MRI, Cardiac properties including the ventricle dimensions, stroke volume, and ejection fraction can be extracted based on accurate segmentation of the left ventricle (LV) myocardium. One of the most advanced segmentation methods is based on fully convolutional neural networks (FCN) and can be successfully used to do segmentation in cardiac cine MRI slices. However, the temporal dependency between slices acquired at neighboring time points is not used. Here, based on our previously proposed FCN structure, we proposed a new algorithm to segment LV myocardium in porcine short axis cardiac cine MRI by incorporating convolutional long short-term memory (Conv-LSTM) to leverage the temporal dependency. In this approach, instead of processing each slice independently in a conventional CNN-based approach, the Conv-LSTM architecture captures the dynamics of cardiac motion over time. In a leave-one-out experiment on 8 porcine specimens (3,600 slices), the proposed approach was shown to be promising by achieving average mean Dice similarity coefficient (DSC) of 0.84, Hausdorff distance (HD) of 6.35 mm, and average perpendicular distance (APD) of 1.09 mm when compared with manual segmentations, which improved the performance of our previous FCN-based approach (average mean DSC=0.84, HD=6.78 mm, and APD=1.11 mm). Qualitatively, our model showed robustness against low image quality and complications in the surrounding anatomy due to its ability to capture the dynamics of cardiac motion.
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.