## 4 Results
### Exploratory Analysis of Spleen MRI Segmentation  
The primary objective of this study is to compare segmentation efficacy and prompting strategies within lung CT and X-ray datasets. This focus is motivated by the fact that CT and X-ray are among the most commonly used imaging modalities in radiological workflows. Additionally, it enables direct benchmarking against previously published studies, including our own prior work. Nevertheless, in this section, we briefly explore the applicability of SAM-based models to other imaging modalities and anatomical regions. This is not intended as an exhaustive evaluation across all modalities or organs but rather as a preliminary step to inform future research directions. In particular, we analyze the segmentation capabilities of SAM and SAM 2 on spleen MRI images. For this exploratory analysis, we selected the clinical abdominal organ segmentation dataset AMOS [29], which comprises 500 CT and 100 MRI volumes. Each of these volumes includes multiple segmentation labels corresponding to various abdominal organs, such as the spleen. We chose the spleen as the segmentation target to assess the performance of an  
Figure 14: Shared seed video results ([PERSON] et al. [19], [PERSON] et al. [21]). Figure 13: Shared seed video results. (**a**) Coronacases shared seed. (**b**) Coronacases proper seed. (**c**) Radiopaedia shared seed. (**d**) Radiopaedia proper seed. organ different from the lungs and MRI to evaluate the models on a modality distinct from the CT data used in the primary comparison. The spleen's relatively simple morphology and lack of pronounced concavities make it a suitable candidate for this exploratory segmentation task. To evaluate the segmentation performance of SAM and SAM 2 specifically on MRI data, we selected 14 volumes labeled as MRI. To ensure representativeness and reduce potential biases related to MRI acquisition devices, we further narrowed the selection to a subset of 8 volumes acquired using a Siemens Ingenia scanner. Figure 15 shows an example of an MRI slice from the AMOS dataset, illustrating the spleen segmentation result produced by SAM 2. The segmentation methodology follows the approach described previously in Sections 3.4 and 3.5. In this exploratory evaluation, we assessed four segmentation configurations. First, we applied SAM with individual prompts provided for every slice across the eight selected MRI volumes. Second, we evaluated SAM 2 using the same per-slice prompting strategy. Third, we tested a propagation-based approach with SAM 2, where segmentation was initialized using a single prompt on the central slice of each volume. Finally, we explored a variant of this propagation-based strategy in which the seed prompt was shared across all volumes, aiming to further reduce the prompting effort by using a single prompt for the entire dataset. Figure 16 summarizes the MRI segmentation performance of SAM and SAM 2 on the AMOS dataset. On the one hand, the X-axis in Figure 16 compares the performance of SAM and SAM 2 in image processing mode using the Jaccard index and Dice score. On the other hand, Figure 16 compares the performance of SAM 2 in video processing mode, using either the middle slice of each volume as a seed prompt or a shared seed prompt for the entire dataset. For this subset of the dataset, which includes eight volumes and a total of 1633 slices, SAM requires 1633 prompts to achieve complete segmentation, whereas SAM 2 in video processing mode reduces this requirement to only 8 prompts, one per volume for the middle-slice seed and one for the entire dataset in the shared-seed configuration. The Y-axis displays the corresponding metric values, with each box representing the distribution per dataset and metric. Outliers are omitted for clarity. Solid horizontal green lines denote median values, dashed horizontal lines indicate mean values, and notches represent 95% confidence intervals.