## 2 Materials and Methods  
A systematic literature review was conducted to explore the state-of-the-art in breast lesion segmentation. This research focused primarily on studies from the past decade to highlight the recent proliferation of DL techniques in this field. Before limiting the search time window to the last decade a rapid search was made for the same query includinga larger time window. In Figure 1 the number of papers included every year shows an increasing trend, with a first peak in 2013 and other peaks approaching the twenties. For this reason and to focus on recent advancements only the window around 2013-2024 was considered.  
Studies published between January 2013 and July 2024 were searched in the PubMed database (US National Library of Medicine, [[http://ncbi.nlm.nih.gov/pubmed](http://ncbi.nlm.nih.gov/pubmed)]([http://ncbi.nlm.nih.gov/pubmed](http://ncbi.nlm.nih.gov/pubmed)), accessed on 31 July 2024). The research query included the terms \"breast MRI lesion segmentation\" to identify studies involving any type of breast lesion, benign or malignant, detectable by any MR imaging modality. Each record (title and abstract) retrieved was manually screened by a reviewer, and ineligible records were removed. The selection procedure is illustrated in the flowchart in Figure 2, following the PRISMA 2020 guidelines [30]. The initial search yielded 359 records for the specified keywords and time interval. Before screening, records were checked for duplicates (0 cases). Papers not in English were removed to avoid language barriers and potential misunderstandings from poor translation (6 cases), and papers without accessible full texts were also excluded (21 cases). This left 332 records to be screened based on titles and abstracts. Records deemed off-topic, such as those involving clinical trials, case reports, general reviews, or studies related to different organs and imaging modalities, were discarded (157 cases). Additionally, articles that only included manual segmentation were excluded (78 cases), as well as studies focused on segmenting the whole breast tissue rather than the lesion itself (4 cases). This process resulted in 93 papers assessed for eligibility. After a full-text review, records that lacked specificity regarding segmentation, either using it only as an intermediate step without reporting segmentation performance or failing to provide proper validation, were excluded (61 cases). Moreover studies that did not use any machine learning techniques were excluded (3 cases). Studies employing automatic segmentation techniques with very small datasets (\(\leq\)20 patients) were also removed, as small sample sizes can lead to unreliable and non-generalizable results (7 cases). Ultimately, 21 articles were included in the review. All the included studies were approved by the respective local institutional review boards. Written informed consent was obtained for most cases, ensuring that no personal information could be identified. In other cases, informed consent was not required as the data were either fully anonymised or publicly available. The articles were analysed by examining the aim of the segmentation task, the type of model used, the MRI sequences employed with consideration for multiparametric models, and the sample size and data quality, with attention to the generalizability and flexibility of the proposed pipelines. The results and discussion are presented in the subsequent sections.  
Figure 1: Trend in the number of indexed studies on PubMed related to “breast MRI lesion segmentation” over the years. The graph illustrates the growing interest and research activity in this field.