DeepVEST: Deep Learning-based Vessel Segmentation and Erasure in Breast MRI for Improved Lesion Assessment.

Abstract

Purpose To investigate whether selective removal of vascular structures can improve lesion visibility and interpretability in maximum intensity projection (MIP) images derived from dynamic contrast-enhanced MRI. Materials and Methods A retrospective analysis was conducted using breast MRI scans from the Duke-Breast-Cancer-MRI (Duke) dataset for model development. DeepVEST, a deep learning method for automatic vessel segmentation and removal, was developed. A reader study with five breast radiologists was conducted to evaluate the impact of vessel-removed MIPs on lesion assessment, using images from the Duke and Advanced-MRI-Breast-Lesions (AMBL) datasets. Segmentation performance was evaluated against manually annotated vessel segmentations using the Dice similarity coefficient (DSC), and perceived usefulness and quality were measured through reader study metrics. Interreader agreement on vessel removal quality and artifact impact was calculated using the Gwet agreement coefficient (AC1). Results DeepVEST achieved a DSC of 0.611 for vessel segmentation. In the reader study (150 assessments, 600 responses), vessels partially or fully obscured lesion margins in 91 of 150 assessments (60.7%). Vessel removal effectiveness was rated 3.820 ± 0.749 on a 5-point Likert scale. Artifacts were reported in 45 of 150 assessments (30.0%), with a low average severity score of 0.493 ± 0.900 (scale 0-5), indicating minimal image quality impact. Substantial interreader agreement was observed for vessel removal quality (AC1 = 0.703) and artifact evaluation (AC1 = 0.736). Conclusion DeepVEST enabled automatic vessel segmentation and removal in breast MRI MIP images, showing high effectiveness with minimal artifacts. Most readers found it helpful for lesion assessment in challenging cases. ©RSNA, 2026.