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Context: Quantitative results from the application of the DeepMedic, the CRF and an ensemble of three similar networks on the training data are presented in Table 2. The latter two offer an improvement, albeit fairly small since the performance of DeepMedic is already rather high in this task. Also shown are results from previous works, as reported on the online evaluation platform. Various settings may vary among submissions, such as the pre-processing pipeline or the number of folds used for cross-validation. Still it appears that our system performs favourably compared to previous state-of-the-art, including the semi-automatic system of Bakas et al. (2015) (bakas1) who won the latest challenge and the method of Pereira et al. (2015) (peres1), which is based on grade-specific 2D CNNs and requires visual inspection of the tumor and identification of the grade by the user prior to segmentation. Examples of segmentations obtained with our method are shown in Fig. 12. DeepMedic behaves very well in preserving the hierarchical structure of the tumor, which we account to the large context processed by our multi-scale network.

Question: The authors claims that DeepMedic  behaves very well in preserving the hierarchical structure tumours, is that true ? Have they tried it across different types of varying cases?
Figure 12 shows successful cases of segmentation for the hierarchy of brain tumors. As seen in Figure 12, the model understands that the sequence of layers goes from oedema to non-enhancing core to enhancing core to necrotic core, preserving the hierarchical structure of tumors. They also show a relatively unsuccessful case where oversegmentation occurs, but even in this example, the hierarchy of the tumor is preserved.