###### Abstract  
Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (sFCM). They represent well-established pattern recognition techniques that are still widely used in clinical research. Starting from the basic versions of these segmentation approaches, during our analysis, we identified the shortcomings of each of them, proposing improved versions, as well as developing ad hoc pre- and post-processing steps. The obtained experimental results, in terms of area-based--namely, Dice Index (DI), Jaccard Index (JI), Sensitivity, Specificity, False Positive Ratio (FPR), False Negative Ratio (FNR)--and distance-based metrics--Mean Absolute Distance (MAD), Maximum Distance (MaxD), Hausdorff Distance (HD)--encourage the use of unsupervised machine learning techniques in medical image segmentation. In particular, fuzzy clustering approaches (namely, FCM and sFCM) achieved the best performance. In fact, for area-based metrics, they obtained DI = 78.23% \(\pm\) 6.50 (sFCM), DI = 65.90% \(\pm\) 8.14 (sFCM), sensitivity = 77.84% \(\pm\) 8.72 (FCM), specificity = 87.10% \(\pm\) 8.24 (sFCM), FPR = 0.14 \(\pm\) 0.12 (sFCM), and FNR = 0.22 \(\pm\) 0.09 (sFCM). Concerning distance-based metrics, they obtained MAD = 1.37 \(\pm\) 0.90 (sFCM), MaxD = 4.04 \(\pm\) 2.87 (sFCM), and HD = 221 \(\pm\) 0.43 (FCM). These experimental findings suggest that further research would be useful for advanced fuzzy logic techniques specifically tailored to medical image segmentation. Keywords:medical image segmentation; breast cancer; pattern recognition; machine learning; clinical feasibility; magnetic resonance imaging; computer-assisted segmentation +
Footnote †: journal: Medical Image Segmentation
images that convey detailed information about the anatomy and physiology of the imaged organs [2]. Moreover, many imaging-enabled tools were developed, supporting clinicians in several tasks of the care process: assisted segmentation [3; 4], diagnosis support [5], treatment response assessment [6], radiomic analyses [7; 8]. For these reasons, computer-assisted image analysis is considered an essential instrument in the clinical workflow [9; 10]. Despite the technological progress characterizing the modern era, some medical tasks, such as image annotation, are still performed manually, often via time-consuming and operator-dependent procedures. As an example, the Gross Tumor Volume (GTV) segmentation for radiotherapy treatments is usually delineated by means of a fully manual procedure [11; 12]. Considering that dozens of slices have to be contoured, this manual process is extremely time-consuming. Moreover, operator dependence is critical in terms of result reproducibility. Indeed, these manual procedures are strongly dependent on clinician's knowledge and experience: this means that a remarkable intra- and inter-operator variability can seriously affect the segmentation and quantification results. In this scenario, computer-assisted approaches (automatic or semi-automatic) allow us to mitigate some of the typical drawbacks of manual procedures. Semi-automatic segmentation techniques involve a minimal level of user interactions and exploit automated algorithms to produce accurate and repeatable results. User interaction, for example, may involve the selection of an approximate initial ROI, which is subsequently used to segment the image [13].