Poster + Paper
2 April 2024 Segmentation of cerebral digital subtraction angiography (DSA) images in idiopathic intracranial hypertension and venous sinus stenosis: evaluating the efficacy of the segment anything model (SAM) and MedSAM
Author Affiliations +
Conference Poster
Abstract
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Amirkhosro Kazemi, Dale Ding, MJ Negahdar, Isaac Josh Abecassis, and Amir A. Amini "Segmentation of cerebral digital subtraction angiography (DSA) images in idiopathic intracranial hypertension and venous sinus stenosis: evaluating the efficacy of the segment anything model (SAM) and MedSAM", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 129302G (2 April 2024); https://doi.org/10.1117/12.3009709
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KEYWORDS
Image segmentation

Data modeling

Angiography

Medical imaging

Image processing

3D modeling

Visualization

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