## 1 Introduction
### Background on Segmentation  
After image acquisition, images are analysed by expert radiologists who identify suspect tumorous tissues, which can be either localised or involve multiple sites. It is crucial to define and delineate the ROI of a lesion to measure tumour size and extent accurately. Monitoring changes in size can be instrumental in determining prognosis and assessing the response to therapy. Additionally, the delineation of the ROI forms the foundation of the radiomic process, enabling the development of customised ML models for various purposes. Segmentation can be used for classification to distinguish between malignant and benign lesions, differentiate various molecular alterations, assess and predict disease progression, and estimate potential treatment outcomes. In this way, it aids in selecting the most appropriate therapy and personalising medical procedures to enhance patient care. Depending on the complexity of the task, different segmentation methods are employed. Traditionally, manual segmentation is regarded as the gold standard, but it is highly time-consuming and limits the creation of large datasets, which are essential for training complex models. To address the challenges of time efficiency and reproducibility, various semi-automatic and fully automatic procedures have been developed and implemented over time. For simpler tasks, such as segmenting whole organs, traditional techniques that do not require ML are often used. These methods include image thresholding, atlas-based segmentation, active contour models, and region growing techniques. When the object of interest is a small lesion or a portion of tissue with an irregular shape, multiple fragments, and intensity variations, more complex methods involving ML have proven to be more effective, as in the case of breast lesions. ML, closely related to data science and statistics, focuses on extracting valuable information from data and making predictions. It can be considered a subset of AI that develops algorithms capable of learning automatically from data. Traditionally, ML is divided into two frameworks: supervised learning, where both the input and the desired output are used to train the model, and unsupervised learning, where algorithms learn patterns from data without any label information. In the context of segmentation, the main advancements have been made with unsupervised clustering techniques and more sophisticated supervised and unsupervised deep learning algorithms. Clustering is one of the most used unsupervised ML methods capable of uncovering structures in unlabelled datasets. Pixels are associated with different clusters based on customised similarity or distance measures. Clustering comes in different variations. A simple but effective approach is K-means, in which data is partitioned into K clusters using K initial centroids. The algorithm works as a minimisation problem, optimising a given cost function, for instance, based on the sum of the squared differences between cluster means and pixel intensity values. Alternative distance definitions, other than the Euclidean distance can also be used. The choice of K and centroids is made at the beginning, using, for instance, random values, and points are associated with the closer centroid. The procedure then iteratively refines the clusters using expectation maximisation until the chosen convergence criterion is met [22]. Despite being efficient and always ensuring convergence to a local minimum [22], k-means also has some drawbacks because the results strongly depend on the initialisation of centroids. Moreover, computational complexity is important to consider as it depends on the number of data elements, clusters and iterations [23]. K-means is regarded as a hard clustering technique, in which each point belongs to one and only cluster.