## 3 Results  
Figure 2 shows segmentation of different regions of the heart at end-diastole and end-systole for three ACDC test subjects using the proposed algorithm (left) and the combined method (right). We observed that direct application of the trained model to the 10 fine-tuning subjects yielded DSC of 0.77, 0.90 and ASSD of 2.32 mm, 2.88 mm for LVM, LVC; these accuracies were improved to 0.82, 0.92 for DSC and 1.97 mm, 1.91 mm for ASSD by the proposed CKC algorithm (data not shown), which were used to fine-tune the pre-trained model. As shown in Table 1, for the 90 test subjects the proposed algorithm yielded DSC of 0.81 \(\pm\) 0.09 for LVM and 0.90 \(\pm\) 0.09 for LVC. Meanwhile, the combined method generated DSC of 0.78 \(\pm\) 0.08 and 0.87 \(\pm\) 0.12 for the two regions, higher than the naive method and DeepLabV3+. Similarly, the proposed algorithm yielded substantially lower ASSD compared with the naive method, which further outperformed the combined method and DeepLabV3+. Of note, the DSC and ASSD provided by our approach were significantly different from each of the other algorithms (\(p<0.0001\)), and the naive method demonstrated higher  
Figure 2: Representative segmentation of different regions of the heart at end-systole and end-diastole for three unseen ACDC test subjects (Subject1, Subject2, and Subject3) using the proposed (left) and the combined (right) methods. Algorithm and manual segmentation are shown in purple and yellow, respectively. ED: end-diastole; ES: end-systole. overall segmentation accuracy than the combined method and DeepLabV3+. As shown in Table 1 in Appendix A, MCD, spatial augmentation, and style-intensity augmentation each improved the segmentation accuracy and the combination of the three components, which constitutes the combined method, provided the highest segmentation accuracy among all the possible combinations. Table 2 summarizes the LV functional parameters generated by manual and algorithm segmentation, illustrating that LV function measurements provided by the proposed approach are closer to manual results compared with the other methods. For example, paired \(t\)-tests showed that LV indices provided by the proposed approach were not significantly different from manual measurements, whereas the measurements generated by the naive and combined method were significantly different from manual LVMM (Proposed: \(p\) = 0.1976; Naive: \(p\) < 0.0001; Combined: \(p\) = 0.0023), LVEDV (Proposed: \(p\) = 0.8015; Naive: \(p\) < 0.0001; Combined: \(p\) < 0.0001), LVESV (Proposed: \(p\) = 0.8631; Naive: \(p\) < 0.0001; Combined: \(p\) < 0.0001), LVSV (Proposed: \(p\) = 0.6617; Naive: \(p\) = 0.0734; Combined: \(p\) < 0.0001), and LVEF (Proposed: \(p\) = 0.2495; Naive: \(p\) = 0.0059; Combined: \(p\) < 0.0001). Table 3 and Figure 3 show that there were strong and significant correlations between the proposed algorithm and the naive method vs. manual analyses of LVMM (Proposed: r = 0.86, \(p\) < 0.0001; Naive: r = 0.79, \(p\) < 0.0001), LVEDV (Proposed: r = 0.99, \(p\) < 0.0001; Naive: r = 0.98, \(p\) < 0.0001), LVESV (Proposed: r = 0.99, \(p\) < 0.0001; Naive: r = 0.98, \(p\) < 0.0001), LVSV (Proposed: r = 0.92, \(p\) < 0.0001; Naive: r = 0.84, \(p\) < 0.0001), and LVEF (Proposed: r = 0.93, \(p\) < 0.0001; Naive: r = 0.75, \(p\) < 0.0001). Please note that the correlations between the naive and combined methods vs. manual measurements were very similar for all the LV indices except for LVMM. [PERSON]'s \(z\)-transformations showed that the correlations for the proposed algorithm and the naive method vs. manual measurements were significantly different for LVMM (\(p\) = 0.0366), LVEDV (\(p\) = 0.0214), LVESV (\(p\) = 0.0214), LVSV (\(p\) = 0.0151), and LVEF (\(p\) < 0.0001). Similar results were observed when comparing the correlations yielded by the proposed algorithm and the combined method.