## 5 Discussion  
Medical image segmentation is critical in clinical applications, such as diagnosis and treatment planning. Ever since computers have been able to work in image processing, this task has been attempted to be automated. The application of artificial intelligence to this task has been a priority since its resurgence at the turn of the century [32]. Recent advances in deep learning have significantly improved the accuracy and efficiency of segmentation methods, particularly those that require minimal user input. Deep learning-based interactive segmentation methods, such as those incorporating Convolutional Neural Network (CNN)s with user inputs like bounding boxes and scribbles  
Figure 16: Video results ([PERSON] et al. [30], [PERSON] et al. [31]). like [33], have demonstrated increased accuracy and robustness, especially for previously unseen objects. Due to their zero-shot learning features, these methods require fewer user interactions and less time than traditional methods. Techniques such as geodesic distance transforms and resolution-preserving networks further improve the accuracy of interactive segmentation by refining the initial automatic segmentations with minimal user intervention [34]. A deep learning-based interactive segmentation method that achieves accurate results with fewer user interactions is proposed in [35]. The authors claim their method consumes less time and generalizes well to previously unseen objects in medical images. In order to do so, they first encode user-provided interior margin points via a CNN to achieve a good initial segmentation result of both previously seen and unseen objects. Then, an information fusion method combines the initial segmentation with a few additional user clicks to refine the segmentation. Knowledge distillation techniques have been employed to transfer knowledge from large, well-trained networks for medical image segmentation to smaller, lightweight networks. This approach maintains high segmentation accuracy while significantly reducing computational complexity and storage requirements. To cite just one example, [36] improved a lightweight network's segmentation capability by up to 32.6%. The integration of SAM 2 into clinical workflows, particularly in radiotherapy, aligns with the increasing demand for efficiency and precision in oncology. Radiotherapy is a complex, multi-step process involving imaging acquisition, contouring of anatomical structures, treatment planning, delivery, and follow-up, requiring significant expertise and coordination among specialists [37]. In resource-constrained settings, many of these steps remain highly manual and time-consuming. SAM 2 could support the automation and standardization of critical stages such as segmentation and verification, thereby reducing clinicians' workload and enhancing treatment consistency. By accelerating processes like image acquisition and contouring, SAM 2 may also help minimize the time patients spend undergoing imaging procedures, thus reducing discomfort and limiting unnecessary exposure to ionizing radiation. This fits into a broader trend toward the adoption of AI-based tools [38], such as those developed in initiatives like Medical Open Network for AI (MONAI) [39] or Open Health Imaging Foundation Viewer (OHIF) [40], which are being successfully integrated into image-based workflows to improve diagnostic accuracy and therapeutic planning. We aim to enable specialists to perform medical image segmentation with as little interaction as possible at the lowest possible cost. All the work mentioned in this section has one thing in common: the need for considerable resources to develop, reproduce, or use it. In order to develop deep learning networks, it is necessary to have a team of professionals dedicated exclusively to it, with access to first-rate data and computational resources. Fine-tuning an existing neural network is less demanding than creating one from scratch. However, it is still a process within the reach of a few.