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Semantic segmentation plays an important role in enhancing the diagnostic accuracy from clinical angiographic images. We analyzed 800 cerebral diagnostic subtraction angiography images from 40 patients with Idiopathic Intracranial Hypertension (IIH) and Venous Sinus Stenosis (VSS) using the Segment Anything Model (SAM) with point and box prompting and MedSAM with box prompting techniques. Despite complexities in the pre-stent images, SAM consistently performed well. In comparison to expert delineated segmentations, SAM's segmentations yielded favorable results with a DSC of 0.91 and an Intersection over Union (IoU) of 0.84 for post-stent images, indicating SAM's robust capability in segmenting these images. Post-stent enhanced contrast opacification boosted SAM's segmentation performance in DSA images, indicating contrast's critical role in post-stent imaging. Our study demonstrates potential utility of out-of-the-box foundation models, SAM and MedSAM, in medical image analysis, a step towards advanced segmentation tools in clinical settings.
Kazemi et al. (Fri,) studied this question.