## 4 Discussion
This choice is prevalent in studies that use segmentations as a baseline for radiomic analysis, where the focus is exclusively on lesion slices. However, it represents a clear limitation in performance evaluation for more general segmentation tasks. As remarked by [PERSON] et al. [36], models that have never been exposed to lesion-free slices might not be immediately generalisable and may require further tuning with normal tissue before being applied to different tasks and datasets. [PERSON] et al. [37] further highlighted this issue, showing that scores obtained solely on lesion slices tend to overestimate the model's performance on broader segmentation tasks. To ensure comparability, results should be reported consistently, either using the convention of lesion-only slices/patches or by including normal tissue portions in the evaluation. However, not all studies explicitly state their approach, making direct interpretation of the results challenging. The inclusion of normal tissue patches and slices should be accounted for in model customisation by selecting appropriate loss functions that handle lesion-free samples effectively and by employing balancing techniques to manage class imbalance, as demonstrated in the training scheme proposed by [PERSON] et al. [37]. Despite the remarked differences, general agreement in terms of preprocessing steps was found in most of the studies. Instead of manually selecting a bounding box around the lesion, to achieve a fully automatic procedure, it is useful to first perform breast segmentation and then perform lesion segmentation only within the breast ROI. This step is essential to prevent false positives due to enhancement in the heart region and other possible confounding organs with similar enhancement. Moreover, this step also serves to reduce the computational burden of considering the whole image. For this purpose, some studies also employed division into left and right breasts to double the training sample size in a natural manner or the use of even smaller boxes. However, this approach, while benefiting from one side, removes the possibility to use bilateral breast symmetry to evaluate possible false positives due to intrinsic parenchymal enhancement, as mentioned by [PERSON] [35] and [PERSON] [47]. A good compromise might be represented by more complex models involving multiple streams for lesion detection, boundary characterisation, and segmentation, as for instance the one proposed by [PERSON] et al. [49]. Moreover, when using multiple images, registration, normalisation, and resampling are often necessary. For deep learning approaches, data augmentation also represents a key step to train the model with its full potential and to improve its generalisation capabilities. Data augmentation usually involves small random rotations, scaling, shifting, mirroring, elastic deformations, and variations in grey-level distribution. In terms of postprocessing, in some cases, small components were ignored by posing a threshold on lesion size or keeping only the larger fragments. However, traditional ML approaches to classify the proposed lesions as normal or suspicious could be used, as for instance made by [PERSON] et al. [35]. For DL approaches, the tuning of the binarisation threshold is also essential to correctly balance false positives and undetected lesions. Considering the whole corpus of studies, large attention was placed on CNN based on U-Nets, which, through continuous improvements, are achieving higher and higher performances. To improve segmentation in such unbalanced images, different loss functions were introduced apart from the classical binary cross-entropy, such as dice loss, focal loss, Tversky loss, and combinations of multiple loss functions (mainly binary cross-entropy and dice), together with the introduction of L2 regularisation terms.