# Explainable Liver Segmentation and Volume Assessment Using Parallel Cropping
## 7 Conclusions  
This study analyzes automatic and semiautomatic parallel cropping techniques used to segment and assess the volume of the liver in medical imaging. U-Net produces unwanted segmentation outside the liver's RoI. However, this was effectively filtered, which increased the dice scores. The average percentage of unwanted data using semiautomatic cropping is \(90\pm 3.4\%\). This implies that only 10% of data are useful from CT volumes. Semi-automatic cropping successfully eliminated irrelevant data. Our proposal improves the accuracy and significantly reduces unnecessary computations. Applying our proposed method to U-Net for liver segmentation, the dice score impressively rises from 0.52 to 0.94, demonstrating a significant leap in accuracy and the potential to drastically improve clinical outcomes. The semi-automatic cropping technique demonstrated higher robustness when dealing with low-quality images with unclear liver boundaries. This approach ensures that cropping remains precise and efficient even when fully automated techniques fail to identify the correct RoI. The proposed persistence-based approach was \(2.29\times\) faster than traditional slice-based cropping methods, because our voxel-based approach computes cropping boundaries in a single pass across all dimensions. Although the focus was on liver segmentation, the results indicate that this method can also be applied to other areas of medical imaging, such as the volumetric assessment of blood vessels, skin, and lungs. DL models are popular but operate as _black boxes_. The decision-making process behind the segmentation results is not interpretable, making it difficult to understand why the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Liver Volume Assessment**} & \multicolumn{2}{c}{**Time (ms) for Cropping**} & \multicolumn{1}{c}{**Time (s)**} \\ \hline
**Size (Grown Voxels)** & **Cropped Size** & **Non PT and Slice** & **PT and Voxel** & **Vol. Assess.** \\ \hline \(512\times 512\times 816\) & \(285\times 229\times 190\) & 113.5 & 44.6 & 0.55 \\ \((3,097,775)\) & \(285\times 229\times 190\) & 113.5 & 44.6 & 0.55 \\ \hline \(512\times 512\times 629\) & \(512\times 629\) & \(264\times 242\times 227\) & 84.87 & 36.65 & 0.63 \\ \((3,681,197)\) & \(264\times 242\times 227\) & 84.87 & 36.65 & 0.63 \\ \hline \(512\times 512\times 671\) & \(326\times 290\times 233\) & 97.02 & 55.01 & 1.08 \\ \((4,588,020)\) & \(326\times 290\times 233\) & 97.02 & 55.01 & 1.08 \\ \((512\times 512\times 722\) & \(305\times 257\times 300\) & 102.48 & 46.86 & 1.28 \\ \hline \(512\times 512\times 841\) & \(344\times 203\times 264\) & 111.80 & 47.87 & 0.98 \\ \((4,068,110)\) & \(290\times 228\times 249\) & 99.10 & 48.51 & 0.96 \\ \hline \(512\times 512\times 836\) & \(308\times 271\times 180\) & 106.31 & 45.10 & 0.68 \\ \((3,373,371)\) & \(253\times 262\times 190\) & 108.11 & 40.98 & 0.55 \\ \((3,120,233)\) & \(284\times 220\times 195\) & 105.30 & 42.67 & 0.61 \\ \hline \(512\times 512\times 856\) & \(186\times 252\times 203\) & 106.80 & 44.02 & 0.49 \\ \((1,092,424)\) & \(103.53\) & 45.23 & 0.78 \\ \hline \multicolumn{1}{c}{} & Std. Dev. & 8.31 & 4.9 & 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison between GPU approaches for LiTS liver dataset [20; 45]. model produces certain outputs, especially in edge cases or when anomalies are present. Clinical decisions require transparency and confidence. The lack of interpretability in DL models poses a barrier to clinical adoption, as practitioners might be hesitant to rely on a system that cannot provide clear justifications for its results. Furthermore, incorrect segmentations are difficult to diagnose, and it is difficult to address the cause of the error without a clear understanding of how the model processes the data. This lack of insight makes it challenging to fine-tune or adapt the model for different datasets or clinical environments, limiting its flexibility and robustness.