## 4 Experiments and Results  
### Experimental Environment  
We use the 3 DIRCADb dataset and the LiTS dataset to train and test our model. The LiTS dataset consists of 131 training CT data and 70 testing CT data. The dataset was obtained from six different clinical sites by using different scanners and protocols. Additionally, the resolution of each data point is very different, among which the resolution  
Figure 5: In the segmentation of the liver and tumor, the post-processing method can effectively improve the dice, accuracy and recall, and can reduce the presence of false-positive and false-negative cases.  
of the inter-slice is between 0.55 mm and 1.0 mm, and the resolution of the intraslice is between 0.45 mm and 6.0 mm. For the LiTS dataset, we use 130 data points as training data and 70 data points as test data. The 3 DIRCADb dataset contains 20 intravenous enhanced CT scans, 15 of which include tumors. For the 3 DIRCADb dataset, of which five of the 20 datasets did not contain tumors, we divided the data into different groups for the segmentation of the liver and tumor. For 3 DIRCADb liver segmentation, we use 15 data points as the training set and five data points as the test set, and cross-validation is performed concurrently. For the segmentation of 3 DIRCADb tumors, we use 12 data points as the training set and 3 data points as the test set to test our method, and cross-validation is performed.  
According to the 2017 LiTS challenge's evaluation criteria, the Dice Per Case and Dice Global Score are used to evaluate the segmentation performance of the liver and tumor. Dice Per Case is the average value of each CT Dice Score, and Dice Global is used to combine all CT data into one data evaluation Dice Score. In addition, measures of standard volumetric overlap error (VOE), root-mean-square error (RSME), relative voxel difference (RVD), mean symmetric surface distance (ASD), intersection over union (IoU) are also used to assess the performance of liver segmentation.  
Our model is trained and tested on a machine with three NVIDIA Tesla K80 graphic processing units (GPUs). We set the initial learning rate to 0.001 in training each 2.5D FCN. The parameter \(\omega_{1},\omega_{2}\) of the combined loss function, that is, Formula (7), is set to 1 for network optimization. For the boundary loss function's optimization, we set the initial learning rate to 0.00005, and \(\alpha\) and \(\beta\) are set to 5 and 1, respectively. As shown in Figure 6, \(\beta\) can affect the convergence degree of the boundary loss function, although overall, the boundary loss can always approach a similar value. When the \(\beta\) value is set to 1, the boundary loss can converge to the lowest point. In addition, we set a training accuracy threshold of 0.002. Whenever the training accuracy is no longer improved beyond the threshold, we multiply the learning rate by a factor of 0.5 to attenuate the learning rate. The entire model is implemented by using Python and Pytorch. During the training, we use data scaling of 0.8\(\sim\)1.2 times to prevent overfitting. In the test, the total data prediction time is between the 30 s and 100 s, which depends on the number of slices.