## 4 Results
### Application on the Test Subsets
#### 4.2.2 Qualitative Results  
An overlap of DL predication and GT segmentation contours on six MRI volumes from the test set is shown in Figures 9-11 to visualize the segmentation results. The first  
Figure 8: The confusion matrix for tumor detection on test datasets with a HighResNet cascade network: (**a**) test set #1; (**b**) test set #2.  
Figure 7: The Dice score achieved on test datasets with a HighResNet cascade network. The red color corresponds to the liver and blue to the tumor Dice score: (**a**) test set #1—compared to ground truth mask; (**b**) test set #2—compared to expert corrections applied after the inference.  
two columns correspond to different slices or views that contain tumor segmentations. A 3D model rendered in 3D Slicer for each case is presented in the third column. The red color represents DL prediction, and green is GT or segmentation corrected by a medical expert. Though on the 2D slices, both liver and tumor contours are presented, on the 3D model, only tumor segmentation is presented from both segmentation masks. A 3D model of liver parenchyma is rendered using only a DL prediction mask to make the visualization clearer and focus on the tumor segmentation.  
Figure 10: Two of the best cases from the test set segmentation in terms of the tumor Dice score (test#1—first row, test#8—second row). Green segmentation GT. Red—segmentation from the method.  
Figure 9: Two of the worst cases from the test set segmentation produced by the method in terms of the tumor Dice score: test#3—first row, test#6—second row. Green segmentation GT. Red—segmentation from the method.  
On the two figures below, samples with the lowest and the highest tumor segmentation Dice score from the test set #1 is presented.  
On the MRI volume test#3 and test#6, our method achieved a Dice score of 0.597 and 0.466 for the tumor segmentation. From Figure 9, two missed tumors on test#3 and one false-positive tumor could be observed. From the 2D slices, low over-segmentation of the liver parenchyma is presented. On test#6, the DL method has one missed tumor out of two annotated on the GT. The liver parenchyma mask is slightly under-segmented compared to a GT.  
From the first row of Figure 10, we can observe that in addition to two correctly detected tumors, the DL predicted one false-positive tumor. From 2D slices, over-segmentation on the liver parenchyma and under-segmentation for the tumor borders is presented. The DL method achieved a 0.809 Dice score (test#8) in the second row, demonstrating an over-segmentation for the tumor and liver parenchyma.  
On Figure 11 two MRI volumes from the second subset are presented. Consisting of a high Dice score for the liver parenchyma for all four samples from this subset, volumes shown below were selected by the lowest tumor Dice score. In addition, those two volumes (case #11, case #12) took the longest to correct for the medical expert.  
One out of three tumors were missed on case test#11 (Figure 11, first row). From the 2D slices, we can see that under-segmentation on one of two detected tumors is present, while the liver parenchyma has over-segmentation. In case test#12 DL method missed two out of three tumors (Figure 11, second row). On the found tumor, over-segmentation requires manual correction from the expert. Liver parenchyma did not require a lot of modification and reached a Dice score of 0.993. The main areas requiring the most significant corrections for the liver parenchyma were borders shared with the kidney, bladder, and diaphragm.