## 3 Experiments
### Experiments with V-Net  
In these experiments, we consider an enlarged in-house data set composed of 45 collected MRI scans made available for this second phase of the study. These data are used to fine-tune the V-Net model after a pre-training procedure based on the use of BraTS data, including 750 4D annotated volumes, each representing a stack of 3D images. Each 4D volume has size \(240\times 240\times 155\times 4\), where the first three dimensions correspond to height, width, and depth of a 3D volumetric image. The fourth dimension corresponds to different scan modalities. For all the experiments developed, we pre-processed both in-house and public BraTS data before their use in training, according to the following strategy. To reduce the amount of data to be processed, we cropped original volumes to sub-regions containing a significant amount of brain and tumour tissues. Then, we normalise each data independently by subtracting the mean and dividing by the standard deviation of the cropped brain region; then, we removed the outliers and, finally, we applied a data resizing in the interval [0,1]. Data are augmented by randomly rotating and reflecting image patches to make training more robust. Finally, we randomly extract patches of \(64\times 64\times 64\) voxels sizes from the cropped data. Initially, we conduct experiments aimed to assess the utility of transfer learning. [PERSON] was trained directly using the preprocessed in-house collected data. We considered several learning strategies for \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **DSC** & **P** & **R** \\ \hline
**Slice 1** & SVM & 0.556 & 0.656 & 0.482 \\  & SVM + MO & 0 & 0 & 0 \\ \hline
**Slice 2** & SVM & 0.885 & 0.898 & 0.873 \\  & SVM + MO & 0.940 & 0.926 & 0.953 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dice (_DSC_), Precision (_P_), Recall (_R_) values obtained by the segmentation procedure when processing slices in Figures 8 and 9. Figure 9: First row, from left to right: (**a**) crop of a source slice (Slice 2) of T1c MR Volume with superimposed the contour of metastasis reference mask (dimension: 644 elements), (**b**) Slice of the corresponding Vol. Second row from left to right: (**c**) Segmentation mask produced by SVM, (**d**) refinement by the Morphological Operators. training [PERSON] by varying the number of epochs with a range from 10 to 30 and splitting the data set into 82% for training, 6% for validation and 12% for test. We obtained poor, unacceptable results with accuracy close to zero. We proceeded then, by pre-training V-Net using pre-processed T1c scans of BraTS data. During the experiment, the number of epochs varied from 15 to 200 and the preprocessed dataset was split again into 82% for training, 6% for validation and 12% for test. The \(DSC\) value obtained is equal to 0.53. Performances obtained would not be acceptable for the final segmentation task, but should be considered acceptable for pre-training. The V-Net architecture, pre-trained on glioma segmentation task, was refined using in-house collected data with different learning strategies. Several configurations of V-Net-based procedure were considered by varying epoch. We conceived three experiments addressing the following main questions:  
* How did the V-Net compare with SVM when trained on the same data set? * How did performances could be optimised increasing the number of training data? * How did dimension of lesions influence accuracy of segmentation results? To address the first question, we train and test V-Net with the same strategy used in the second interpatient analysis conducted with SVM (see Section 3.3). Results obtained are shown in Table 5. Comparing these results with those obtained by the SVM, shown in Table 3, we found that SVM segmentation prevails. To address the second question, we used an enlarged dataset composed of 45 T1c volumes, of which 17 for test and the remaining 28 used in 4-fold cross-validation. Results obtained were still poor with accuracy values under the value of 0.50 for all the indexes used.