Validation of UniverSeg for Interventional Abdominal Angiographic Segmentation

Automatic segmentation of angiographic structures can aid in assessing vascular disease. While recent deep learning models promise automation, they lack validation on interventional angiographic data. This study investigates the feasibility of angiographic segmentation using in-context learning with the UniverSeg model, which is a cross-learning segmentation model that lacks inherent angiographic training. A retrospective review, after IRB approval, identified 234 patients who underwent interventional fluoroscopy of the celiac axis with iodinated contrast from January 1, 2019, to December 31, 2022. From 261 acquisitions, 303 maximum contrast images were selected, each generating a 128 × 128 pixel partition for arterial detail analysis and binary mask creation. Image-mask pairs were divided into three classes of 101 pairs each, based on arterial diameter and bifurcation number. UniverSeg was tested class independently in a fivefold nested cross-validation. Performance analysis for in-context learning determined average model convergence for class sizes from 1 to 81 pairs. The model was further validated by repeating the tests on the inverse segmentation task. Dice similarity coefficients for decreasing diameters were 78.7%, 72.5%, and 59.9% (σ = 5.96, 7.99, 14.29). Balanced average Hausdorff distances were 0.86, 0.71, and 1.16 (σ = 0.37, 0.52, 0.68) pixels, respectively. Inverted mask testing aligned with UniverSeg expectations for out-of-context problem sets. Performance improved with support class size, vessel diameter, and reduced bifurcations, plateauing to within ± 1.34 Dice score at N = 51. This study validates UniverSeg for arterial segmentation in interventional fluoroscopic procedures, supporting vascular disease modeling and imaging research.