CoroSAM: adaptation of the Segment Anything Model for interactive segmentation in Coronary angiograms

X-ray Coronary Angiography (XCA) enables visualization of coronary arteries for disease and morphology assessment. Accurate segmentation of major coronary vessels is crucial for automated analysis of their geometric features but presents challenges due to anatomical complexity. This study introduces CoroSAM, an adaptation of the LiteMedSAM Foundation Model, employing parameter-efficient fine-tuning for interactive coronary artery segmentation in XCA images.

The proposed approach incorporates Convolutional Adapter layers within the image encoder's TinyViT blocks to enhance domain-specific feature extraction while maintaining computational efficiency. A point-based prompting strategy directly encodes vessel endpoints and branch points as additional input channels. Model evaluation employed 5-fold cross-validation on the ARCADE dataset and zero-shot testing on external datasets (XCAD, DCA1). Performance was compared with state-of-the-art models for user-guided segmentation.

CoroSAM demonstrated superior performance on the ARCADE test set (Dice=0.87, Precision=0.86, Recall=0.89) while requiring fewer trainable parameters compared to competitive models. Statistical analysis confirmed significant improvements over alternative Adapter configurations. Zero-shot generalization yielded competitive performance on external datasets (XCAD: Dice=0.82; DCA1: Dice=0.73), demonstrating robust transferability across different image qualities.

Integrating specialized Convolutional Adapters and channel-encoded point prompts enables accurate delineation of major coronary vessels with minimal user intervention. CoroSAM's architecture facilitates efficient inference on standard computing hardware without dedicated GPUs, providing a practical tool for clinical applications. This approach establishes an adaptation framework that effectively balances segmentation accuracy with computational efficiency, making it suitable for routine analysis workflows.