Scalable Left Ventricular ROI Annotation for Stress Perfusion Cardiac MRI using Deep Learning with Visual Refinement

Accurate extraction of the left ventricular (LV)-centred region of interest (ROI) in stress perfusion cardiovascular magnetic resonance (CMR) remains challenging due to low signal-to-noise ratio (SNR), motion artefacts, high-dimensional image data, and limited annotated datasets. Efficient ROI localisation is an important preprocessing step for reducing irrelevant anatomical content and improving downstream AI-based analysis. We propose a scalable and annotation-efficient framework for LV-centred ROI localisation and preprocessing in low SNR stress perfusion CMR. A U-Net pretrained on the Multi-Centre, Multi-Vendor, and Multi-Disease (M&Ms) cine CMR dataset was used to generate initial LV localisation proposals on previously unannotated perfusion CMR data acquired at a UK tertiary cardiac centre. Rather than targeting pixel-accurate segmentation, the segmentation outputs were used to identify the LV-centred region, and fit circular ROIs encompassing the LV cavity and surrounding myocardium. A lightweight graphical user interface (GUI) enabled rapid visual assessment, manual annotation and refinement, and temporal propagation across 42-frame sequences. The framework was applied to 798 stress perfusion videos (33,520 frames). A subset of 2882 frames from 69 videos was manually annotated and reviewed using the proposed GUI-assisted framework with confirmation from clinical experts. GUI-assisted annotation required 1-5 min per video (mean ≈ 2.5 min), corresponding to an estimated 8-12 × reduction in annotation time compared with conventional manual annotation workflows. Fine-tuning on the reviewed annotations improved Dice score from 0.87 to 0.90 compared with training from scratch. The proposed framework enables scalable LV-centred ROI annotation and preprocessing in low-quality stress perfusion CMR.