## 2 Related Works  
In [10], the authors presented MedSAM, a fine-tuned SAM model for medical image segmentation that outperforms the default model in 3D and 2D. The Medical SAM Adapter (Med-SA), introduced in [11], enhances SAM specifically for medical image segmentation by incorporating domain-specific medical knowledge and a light adaptation technique. Such is the interest in the application of SAM to the field of medical image segmentation that [12] focuses on presenting a comprehensive overview of the efforts applied to improve the efficacy of SAM in said task. This model was later extended in 2024 with the release of SAM 2, an advanced version capable of segmenting images and videos [8]. Foundation models like SAM are trained on vast datasets. They are versatile tools that can be integrated into various tasks across domains. A notable feature of this kind of model is its zero-shot transfer capability, which allows it to perform segmentation in domains for which it was not explicitly trained [13]. The strength of these models lies in their ability to generalize across tasks, thanks to the extensive training data and the model's capacity to learn abstract features and latent representations, enabling the model to execute tasks accurately, even when encountering previously unseen data. However, each time the specialist needs to segment an image using these tools, there is an inherited cost associated with prompting the same object every time. In the particular case of the medical image segmentation task, this is a downside, as specialist time is a scarce and valuable resource in this domain. This work aims to reduce specialist effort as much as possible. [PERSON] et al. [14] propose a \"One-Prompt Segmentation\" paradigm that bridges interactive and one-shot methods for medical image segmentation. Their approach utilizes a single prompted image to generalize segmentation across unseen tasks without requiring retraining. This is achieved through the integration of a \"One-Prompt Former\", a module designed to efficiently incorporate template features into the segmentation process. Trainedon 64 medical datasets and evaluated on 14 unseen datasets, the method demonstrates robust generalization capabilities. However, its reliance on four distinct prompt types (clicks, bounding boxes, doodles, and segmentation masks) for different anatomical structures necessitates user interaction for each dataset, potentially increasing the complexity of clinical deployment. SAM 2 has attracted considerable interest in the field of medical image segmentation. It has been applied to achieve remarkable results in the segmentation of complex anatomical structures like ocular CT scans, as demonstrated by [15], and in liver CT images [16]. Both studies utilized refined medical SAM 2 models to achieve these advancements. Medical SAM 2 was introduced by [PERSON] et al. [17]. It leverages SAM 2's video segmentation framework to process medical imaging data, including unordered 2D slices and 3D volumes. The key innovation lies in a self-sorting memory bank that dynamically selects and organizes embeddings based on confidence scores and dissimilarity measures. This mechanism enhances segmentation consistency and generalization by prioritizing the most informative embeddings, irrespective of their temporal order. The framework achieves significant performance gains across a wide range of benchmarks. In contrast, our method avoids user-dependent prompt customization, as seen in [14], and complex memory mechanisms, as in [17]. Instead, we employ a single, generalized prompt per dataset, effectively minimizing user interaction while maintaining strong segmentation performance across diverse datasets. By structuring medical volumes as bidirectional, video-like sequences rather than ordering frames based on embedding scores or dissimilarity measures, we leverage SAM 2's capabilities without incurring excessive computational demands. This approach strikes a pragmatic balance between segmentation quality and generalization for clinical applications.