## 4 Discussion
In addition, the derived LV function measurements provided by our approach were strongly correlated with expert manual analyses with no significant differences between the techniques (\(p\) = 0.1976-0.8631, Table 2). This is important because our approach implemented fully automated transfer learning to segment an independent cardiac cine MR dataset acquired using a different MR system at a different location in patients with different cardiac diseases _without_ requiring manual segmentation of the target dataset, potentially enabling efficient clinical workflow and facilitating broader use of deep learning for a wide range of applications. We also implemented a combined method that employed state-of-the-art style-intensity augmentation techniques [17] to address the domain shift issue, which had performed well for breast segmentation in different MRI datasets. Compared with our approach, the optimized implementation of the combined method yielded lower DSC of 0.78 for LVM and 0.87 for LVC with substantially greater ASSD, as shown in Table 1. In another study [18], the authors tackled a similar problem by developing a series of stacked transformations that performed extensive data augmentation (sharpening, blurring, adding noise, changing brightness/contrast, intensity perturbation, rotation, scaling, deformation) during network training. For eight public MRI/ultrasound datasets, the authors achieved improved segmentation accuracy with the use of the proposed data augmentation techniques. These studies [17; 18] showed that advanced and extensive data augmentation techniques yielded higher segmentation performance than adversarial learning-based domain-adaptation techniques. Surprisingly, the naive method generally outperformed the combined method for segmentation accuracy measurements (except for DSC for LVM) but the correlations of LV function measurements with manual results were comparable. This warrants further investigation. Of note, the well-known DeepLabV3+ algorithm [30] performed poorly in this work (see Figure A1), further highlighting the challenges of domain shift for medical image segmentation. In fact, we previously trained the DeepLabV3+ model on 50 subjects from the UK Biobank dataset and applied the model to segment 50 previously unseen ACDC subjects. We achieved DSC of 0.437 for LVM and 0.568 for LVC. Similarly, we trained the DeepLabV3+ model on 50 ACDC subjects and tested the model on 50 subjects from the UK Biobank dataset. We obtained DSC of 0.745 for LVM AND 0.813 for LVC. Please note that these results are excluded in the final version of our previous paper [3] as suggested by the reviewers. In a recent study of lung MRI segmentation [23], we achieved DSC of 0.872 and 0.701 by training the DeepLabV3+ model on one dataset and testing the model on another different dataset. Collectively, these and other studies suggest the inability of deep learning, including DeepLabV3+ and other state-of-the-art models, to deal with the domain shift issue for medical image segmentation. Our approach outperformed the naive method and a combined method that used state-of-the-art data augmentation techniques [17] and differs from the other methods [18; 31] in that in addition to comprehensive data augmentation, we implemented Monte-Carlo dropout to mitigate overfitting and a CKC algorithm to automatically update the \"annotations\" of the fine-tuning subjects. Previous studies [3; 24] demonstrated the effectiveness of using CKC to improve CNN initial segmentation and here we substantially extended the previous work by demonstrating its utility in a new application, whereby the CKC post-processing results were incorporated to effectively tune the trained model to segment an independent cardiac cine MRI dataset. The proposed framework is relatively independent from commonly used domain adaptation and data augmentation techniques.