## 5 Conclusions  
In conclusion, from the analysis of the existing corpus of studies, several fundamental gaps have emerged. In particular, the use of DL techniques demands increasingly large amounts of data for both training and testing. However, there is a significant lack of comprehensive, large public datasets that are essential for preventing and reducing bias, as well as improving model generalisation. Collaboration between different centres and institutions is necessary to share data and successfully validate models, paving the way for practical clinical applications. One of the main challenges in building large datasets is the time-consuming nature of manual segmentation required to provide accurate labels. A potential solution is to further investigate weakly supervised models that require minimal effort to produce a significantly smaller amount of labelled slices, along with the implementation of fully unsupervised deep learning approaches. Operator variability remains a source of bias in model training, further reinforcing the need to reduce human involvement by adopting unsupervised models capable of learning without strong human bias. Public datasets should include multicentric data from different scanners, offering variability in techniques, multiparametric data, and full images containing both normal tissue and lesion slices. Such datasets should also feature a variety of mass and non-mass lesions, variations in parenchymal composition, diverse ethnicities, cancers of small size, and patients with implants or different breast morphologies. To better capture inter-slice features and obtain comprehensive information, the use of 3D models with multiparametric data is recommended. However, this approach further underscores the need for larger and more diverse datasets. Achieving generalisability will require coordinated efforts to build shared datasets, along with the production of open-source codes and the availabil  
Figure 3: Summary of the main stages and options in the breast lesion segmentation process. ity of pre-trained models. These resources could facilitate transfer learning and domain adaptation, ultimately advancing the field toward more robust and generalisable solutions. The landscape of ML applications for segmentation is vast. However, the possibility of any model being clinically applied must go through several essential steps. Before integration into clinical practice, further testing of the model's capabilities on external and public datasets is necessary to ensure bias reduction and to identify potential limitations in generalisation. Once models are properly validated and achieve CE/FDA marking, indicating their conformity, they should be implemented as custom modules in pre-existing local software. This ensures they are easy to use and effectively assist clinicians not only with diagnosis and prognosis but also by saving time. This review reported the state of the art for breast lesion segmentation on MR images, exploring the studies published in the last decade, encompassing early unsupervised approaches, traditional classifiers, and diving into deep learning approaches. An increasing interest has grown towards deep learning approaches, especially CNN based on U-Net; however, comparison between studies is still difficult due to differences in images, protocols, and training procedures. However, this review has also some limitations, as it offers only a partial point of view, including results only from the PubMed database; a more complete overview on the topic should also include works that were not indexed on the PubMed database or account for a wider initial keyword selection. This research was funded by Project--PNC-E3-2022-23683266--Advanced Diagnostic (HLS-DA), titled INNOVA  
Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: