## 5 Discussion and Conclusions  
As seen in our experimental context, brain metastases can be automatically segmented using automated processing of T1c scans of MR images. The main goal of the proposed procedure was to facilitate the segmentation task within the radiation therapy workflow, actively supporting radiologist in the delineation of lesions. Our results showed that both a feature-based and a deep network approach are promising for the segmentation of MRI brain metastases achieving both an acceptable, although a not very good level of performance. Nevertheless, it is worth to note that SVM and V-Net achieved best results under quite different conditions. In the inter-patient analysis, SVM appears to be unable to manage a high level of heterogeneity in training, producing best results when trained only on individual cases, plus omitting heterogeneous voxels belonging to necrosis within the pathological area. On the contrary, V-Net model achieved an acceptable level of accuracy when trained on multiple cases and selecting voxels from the entire pathological area. Moreover, the two optimised models show comparable performances, but segmentation results are qualitatively different in many cases, as illustrated in Figure 11. Differences are emphasized when segmenting highly heterogeneous lesions. In line with results obtained in previous works focused on MRI brain tumour segmentation, we found that SVM can capture complex multivariate relationships in the data, but classification results may suffer from spatial inconsistencies. The use of post-processing refinement stages is usually suggested, as in our strategy that contemplates the use of morphological operators. V-Net segmentations are more homogeneous on average and results obtained confirm that no additional processing is required. We identified some limitations of the study. A very small number of in-house data were available, and the interpretation of the results would inevitably have to allow for that. However, as a general consideration, if the use of the deep network, pre-trained on neighbouring but not equivalent data and then fine-tuned with a very small dataset, allowed us to obtain acceptable performances, it is reasonable to think that it is possible to improve these results significantly when a broader dataset becomes available. For SVM, instead, it is difficult to foresee that there will be a benefit from the use of a wider training set having shown limitations in learning from varied heterogeneous data. Moreover, both the classifiers made under-estimation and over-estimation errors when the pathology occupies a small volume (under the 100 elements) and has a heterogeneous context. These errors are because of misclassifications between the vessel and lesion voxels. Probably, the simultaneous use of different MRI modalities could improve performances than when using single MRI modalities as experimented in [27], but the use of multimodal procedure would make it difficult the implementation of the automated segmentation in the radiotherapy clinical practice. Both the learning models showed advantages and drawbacks, but in the light of a great number of data available, a deep learning algorithm appears preferable. The main conclusions of our experimental work can be summarised as follows:  
* SVM shows good performances with a smaller training set, but it is unable to manage a high level of heterogeneity in the data and requires post-processing refinement stages
* The V-Net model shows good performances when trained on multiple heterogeneous cases but requires data augmentations and transfer learning procedures to optimise performances
* Users have found the segmentation strategy implemented either with SVM + Morphological Operator or V-Net, satisfactory and have considered misclassification errors acceptable and manageable with interactive phases devoted to manual refinements of the automated results. The automated procedure is installed in the RT workflow to measure benefits in supporting brain metastasis delineation.