## 3 Results
### Traditional Supervised Models  
Different ways of addressing the segmentation task emerged with the growing interest in supervised learning for classification. Intensity and dynamic features can be associated with a single voxel or a group of voxels of a candidate lesion, allowing the training of binary classifiers able to identify the lesion, as respectively explored by [PERSON] et al. [34] and [PERSON] et al. [35].  
In particular, [PERSON] et al. [34] adopted a multimodal approach exploiting imaging data, including DCE-MRI, DWI, and also \({}^{18}\)F-fluorodeoxyglucose-PET, to detect, segment,and classify both malignant and benign breast lesions. Their procedure involved the extraction of local, intensity-based, and kinetic features, and the training of a Random Forest (RF) classifier able to discriminate lesion and normal tissue voxel-wise. Further preprocessing stages involved the segmentation of the whole breast area based on a region growing algorithm. Intensity-related features were extracted from each voxel and each modality and used to train a RF classifier. For each patient, 1000 randomly selected samples per class were used in the training phase. Feature selection was performed using two methods, Gini importance and minimum redundancy maximum relevance, with the latter obtaining better results and less correlated features. Performances were assessed using leave-one-out cross-validation (LOOCV) on a dataset of 34 subjects, comparing the automatic segmentation with the manual one, obtaining a mean dice of about 0.67 using all the modalities, a dice of 0.618 using DWI and DCE, and a dice of 0.58 using only the DCE, suggesting enhanced performances for multimodal approaches. The multiparametric approach actually improved the segmentation, reducing false-positive cases, identified as blood vessels and parenchymal enhancement.  
Another notable approach by [PERSON] et al. [35] addressed the issue of segmentation of benign and malignant lesions from breast T1w, T2w, and DCE-MRI data. They first generate candidate lesions through 3D morphological sifting and then classify them as normal tissue or lesion using the random under-sampling (RUSBoost) classifier. The preprocessing stage involves the segmentation of the whole breast region on the T1w image using Otsu thresholding and [PERSON]'s algorithm and splitting into left and right breasts, allowing the processing of one breast at a time. Next, from the subtraction image (DCE pre-contrast and first DCE post-contrast), lesion candidates are generated using morphological filters with linear structuring elements at three different scales (1, 1/2, and 1/4). Scaled images also undergo multilevel Otsu thresholding and size thresholding. Finally, features of four categories--morphological, intensity, texture, and enhancement kinetics--are extracted from all the available images (T1w, T2w, and DCE-MRI). The extracted features are used to determine if the candidate is a real lesion or not. The model was trained on 58 subjects, leaving 59 cases for testing. Automatic segmentation is compared to radiologists' manual segmentation, achieving an average dice score of 0.72 \(\pm\) 0.15 with a median of 0.74.