Although commonly used classical data augmentation techniques (e.g., geometrical transformation, noise, contrast and blurring perturbation, histogram equalization and matching) have been widely used in various applications, other advanced and extensive augmentation techniques have also demonstrated effectiveness in addressing the domain shift issue [17; 18]. In particular, recent studies using advanced data augmentation techniques demonstrated higher performance than several adversarial learning-based domain-adaptation methods for several medical image segmentation tasks [17; 18]. Although the previous studies demonstrated some promise in tackling the domain shift issue for medical image segmentation, these algorithms have limitations. For example, domain adaptation using labeled data from a target domain requires a substantial amount of time and expertise for manual annotation and is not compatible with efficient research and clinical workflow. Adversarial learning that transforms a dataset from a source domain to resemble a target domain typically requires a large dataset from the target domain and a long time for re-training/fine-tuning. Data augmentation aims to learn non-domain specific features by performing extensive transformations to change the appearance of training datasets and often generates non-realistic datasets that do not resemble real world cases, which may or may not adversely affect the performance. Another potential issue associated with these techniques is the increased difficulty of algorithm interpretability because of the \"black box\" nature of deep-learning methods. Here, we proposed a different approach to tackling the domain shift issue. In particular, we employed a machine-learning method to automatically segment a subset of an unseen dataset without manual annotations to fine-tune a pre-trained deep-learning model to segment cardiac MRI datasets from a different domain. The proposed approach required 12 min to segment a relatively small dataset of 10 subjects for fine-tuning and to update the pre-trained model without affecting algorithm interpretability. Importantly, our approach yielded several commonly used and clinically relevant LV function measurements that are in strong agreement with expert manual analyses; this was not demonstrated in the previous studies. A preliminary version of this work has been published in conference proceedings [19] and there are substantial differences between the current work and the previous version [19]. In the current version, we reviewed some commonly used techniques (e.g., domain adaptation and data augmentation) that are developed to tackle the domain shift issue and discussed the advantages/limitations of these techniques. We also provided some details regarding the mathematical formulation and upper bound-based iterative optimization of the proposed continuous kernel-cut method. In addition, we implemented several state-of-the-art deep-learning segmentation models (DeepLabV3+ and an optimized style-intensity augmentation method) and performed comprehensive comparison between these methods and our approach. Furthermore, we discussed the study limitations and proposed some future work directions. These elements were not included in our previous work [19] and represents some of the major differences in the current work.