## 4 Discussion  
Deep learning is emerging to potentially transform cardiac imaging workflow and clinical patient care. Here, we developed an approach to employing a trained CNN for LV segmentation and function evaluation in an independent cardiac cine MRI dataset. For a dataset of 180 cine MR images from 90 subjects with various cardiac disease, we made the following observations: (1) improved segmentation accuracy in the independent dataset; (2) strong correlations between the proposed approach and manual analyses of LV indices; and (3) rapid and fully automated fine-tuning procedure _without_ needing manual labels for the independent dataset. Cine MRI has been routinely performed for evaluation of LV structure and function in cardiovascular MR exams. Deep learning and machine leaning have demonstrated promise in several aspects of the cardiac research and clinical workflow, including but not limited to prediction of cardiac left ventricular kinematics and boundaries [36], classification of cardiac arrhythmias from electrocardiogram [37], and detection of cardiac structure and structural abnormalities [38]. However, direct application of a trained model to a previously unseen dataset often yields suboptimal performance [3, 8, 39]. For example, direct application of a CNN trained on a large cine MRI dataset of 4275 subjects [8] to 20 patients in a previously unseen ACDC dataset yielded DSC of 0.65 for LVM and 0.74 for LVC. Previous studies showed that DSC is usually sensitive to small differences when the segmented object is relatively small and not very sensitive to errors when the object is relatively large [40]. We note that the size of LVM is generally smaller than the LVC at end-diastole although the differences between the two regions are smaller at end-systole. A recent study [9] investigated the variabilities of intra and inter-observer manual segmentation. The authors reported greater DSC of 0.956-0.967 for LVC and 0.870-0.900 for LVM at end-diastole, and similarly, these were 0.898-0.941 and 0.891-0.917 at end-systole. The robustness of manual segmentation and the substantially lower algorithm DSC [8] than repeated manual analyses (0.65 vs. 0.870-917) suggest that manual segmentation errors have a minimal effect in this case. In addition, the training and testing datasets used by [PERSON] et al. [8] differ substantially as the training dataset mainly consists of healthy volunteers whereas the testing dataset comprise patients with diverse cardiac pathologies, which affect the appearance of the myocardium in MR images. Based on the literature and our experience, we think that the relatively low DSC for LVM than LVC (0.65 vs. 0.74) reported by [PERSON] et al. [8] is mainly caused by the combined effects of the large differences between training and testing datasets, the relatively small size, hollow shape, and image signal intensity inhomogeneity in the LVM compared with LVC. However, this warrants further investigation. Nonetheless, the initial suboptimal accuracies [8] were later improved by employing 80 manually segmented subjects in the ACDC dataset for fine-tuning. Previous studies [4, 41] and our efforts have shown that manual segmentation of a 3D cardiac MR volume with 10-15 slices typically requires 20-30 min. This lengthy procedure requires experience and expertise from examiners, introduces user variability, and is not compatible with efficient research and clinical workflow. Similarly, another study [3] applied a pre-trained state-of-the-art CNN (1 st place winner in the ACDC segmentation challenge) to 40 ACDC subjects and achieved DSC of 0.78 for LVM and 0.86 LVC. Compared with these previous works, our approach yielded greater DSC of 0.81 for LVM and 0.90 for LVC _without_ requiring manual segmentation of the fine-tuning datasets. In our future work, we will compare the results from this study with that by fine-tuning the proposed algorithm framework using manually segmented unseen dataset in terms of segmentation accuracy and time.