# Explainable Liver Segmentation and Volume Assessment Using Parallel Cropping
## 6 Experimentation and Discussion
### Liver Segmentation  
This study aims to analyze the impact of cropping on liver segmentation. The segmented results using Chan-Vese [16] and U-Net [14] are shown for four slices. We show the liver segmentation on the original CT liver slices in Figures 5-8. The liver segmentation on cropped liver slices is shown in Figures 9-12. We apply the U-Net and Chan-Vese methods to the original and cropped liver slices. In previous figures the Chan-Vese model does not show any unwanted elements in the segmentation; however, the U-Net model presents unwanted elements. Figures 13-16 present a segmentation comparison between the ground truth, the Chan-Vese approach, and the U-Net algorithm obtained from cropped slices 1-4. The impact of cropping on liver segmentation was assessed using both the Chan-Vese and U-Net models. When applied to original CT liver slices, the Chan-Vese model achieved an average dice score of 0.938, with an average processing time of 0.791 s per slice. After applying the cropping technique, the dice score improved to 0.960, and the processing time was reduced to 0.571 s per slice, resulting in an average speedup of 1.387 times. These results affirm that cropping helps to eliminate irrelevant data, focusing computations on the liver, which improves both accuracy and efficiency. The U-Net model, however, initially showed a decrease in performance when tested on cropped slices, with the dice score dropping from 0.521 to 0.431. This decline was attributed to the resizing of cropped slices to match the input size required by U-Net, which impacted accuracy. Nonetheless, after applying post-processing to remove unwanted segmented areas, the dice score increased to 0.956. Although U-Net's dice score is comparable to that of Chan-Vese after post-processing, the Chan-Vese model consistently outperformed U-Net in speed and segmentation accuracy on cropped images. Figure 5: Original CT slice 1. Figure 6: Original CT slice 2. Figure 7: Original CT slice 3. Figure 11: Cropped CT slice 3 (Note: Cropped slices are resized for proper view, as U-Net provides fixed size output). Figure 8: Original CT slice 4. Figure 12: Cropped CT slice 4 (Note: Cropped slices are resized for proper view, as U-Net provides fixed size output). Figure 10: Cropped CT slice 2 (Note: Cropped slices are resized for proper view, as U-Net provides fixed size output). Figure 9: Cropped CT slice 1 (Note: Cropped slices are resized for proper view, as U-Net provides fixed size output). When the slices are cropped, the dice score (defined by [PERSON] et. al. [44]) (0.96 \(\pm\) 0.005) and speedup are improved in the Chan-Vese model, as shown in Table 1. But the dice score is reduced in U-Net-based liver segmentation before post-processing (0.431 \(\pm\) 0.04), as shown in Table 2. Dice score reduction due to cropping in U-Net in Table 2 is due to the fact that the slices are resized again to map the input of the U-Net network. U-Net is trained on original images and tested on original slices. The study examines U-Net trained on cropped images for testing cropped slices. When we perform post-processing after U-Net-based liver segmentation, this takes the maximum component in the image and filter out the other segmented regions. This results in a better dice score (0.956 \(\pm\) 0.006) after post-processing, as shown in Table 2, and the boundaries are more confined to the liver using U-Net-based liver segmentation after post-processing, as shown in the figures dedicated to the cropped CT slice comparison. We do discuss speedups on U-Net, as it takes the fixed-size image as an input to the network. Figure 6:0 Cropped CT slice 4. Figure 6:1 Cropped CT slice 2. Figure 6:2 Cropped CT slice 1. Figure 6:3 Cropped CT slice 3. The dice score is reduced when the input CT slice is cropped using U-Net-based liver segmentation, as shown in Figure 6:4 But it is improved when we apply the post-processing to remove the unwanted segmentation.