## 6 Discussion and Conclusions
In fact, on area-based metrics, they obtained DI = 78.23% \(\pm\) 6.50 (sFCM), JI = 65.90% \(\pm\) 8.14 (sFCM), sensitivity = 77.84% \(\pm\) 8.72 (FCM), and specificity = 87.10% \(\pm\) 8.24 (sFCM), \(\text{FPR}=0.14\pm 0.12\) (sFCM), and \(\text{FNR}=0.22\pm 0.09\) (sFCM). On distance-based metrics, they obtained MAD = 1.37 \(\pm\) 0.90 (sFCM), MaxD = 4.04 \(\pm\) 2.87 (sFCM), and HD = 2.21 \(\pm\) 0.43 (FCM). A second segmentation of the same radiologist or the segmentation of a different radiologist would have certainly allowed us to quantify the inter-/intra-operator variability of the results. Nevertheless, as already observed in [56], the mean DI was 0.81 (range 0.19-0.96). The mean DI is higher for the 'easy tumors' compared to the 'challenging tumors' (0.83 _vs._ 0.75, respectively, \(p<0.001\)). The mean DI for each observer combination separately, for all tumors, ranged between 0.78 and 0.83, where the segmentations of the breast radiologist and the medical student showed the highest overlap. These results confirm that the performance achieved by the best performing methods are in line with the inter-observer agreement, also in terms of metrics variability according to the lesion types. As further developments, we plan to investigate innovative improvements to further improve the performance with fuzzy clustering, by using (_i_) more sophisticated membership functions, and (_ii_) more advanced pre- and post-processing steps. Moreover, investigating and comparing the latest machine learning techniques, such as Generative Adversarial Networks (GANs), for unsupervised detection and segmentation [57; 58] would be relevant with a sufficient amount of data for training and test. Finally, the implementation of multiparametric or multimodal approaches [59], by using different types of co-registered medical images--i.e., Diffusion Weighted Imaging (DWI) and Positron Emission Tomography (PET)/MRI--probably would allow us to improve the detection performance [60; 61]. The following are available online at [[https://www.mdpi.com/article/10.3390/app12010162/s1](https://www.mdpi.com/article/10.3390/app12010162/s1)]([https://www.mdpi.com/article/10.3390/app12010162/s1](https://www.mdpi.com/article/10.3390/app12010162/s1)): Table S1: Dice Index (DI) values obtained by the four investigated unsupervised methods on each of the 50 segmented breast masses on DCE-MRI. In the last row the mean value \(\pm\) the standard deviation is reported; Table S2: Jaccard Index (JI) values obtained by the four investigated unsupervised methods on each of the 50 segmented breast masses on DCE-MRI. In the last row the mean value \(\pm\) the standard deviation is reported; Table S3: Sensitivity values obtained by the four investigated unsupervised methods on each of the 50 segmented breast masses on DCE-MRI. In the last row the mean value \(\pm\) the standard deviation is reported; Table S4: Specificity values obtained by the four investigated unsupervised methods on each of the 50 segmented breast masses on DCE-MRI. In the last row the mean value \(\pm\) the standard deviation is reported; Table S5: False Positive Ratio (FPR) values obtained by the four investigated unsupervised methods on each of the 50 segmented breast masses on DCE-MRI. In the last row the mean value \(\pm\) the standard deviation is reported; Table S6: False Negative Ratio (FNR) values obtained by the four investigated unsupervised methods on each of the 50 segmented breast masses on DCE-MRI. In the last row the mean value \(\pm\) the standard deviation is reported; Table S7: Mean Absolute Distance (MAD) values obtained by the four investigated unsupervised methods on each of the 50 segmented breast masses on DCE-MRI. In the last row the mean value \(\pm\) the standard deviation is reported; Table S8: MaximumDistance (MaxD) values obtained by the four investigated unsupervised methods on each of the 50 segmented breast masses on DCE-MRI.