Automated CTA-based perforator mapping for DIEP flap planning in breast cancer reconstruction

Abstract

Aim: Deep inferior epigastric perforator (DIEP) flap breast reconstruction is considered the gold standard for autologous reconstruction. Preoperative perforator mapping using computed tomography angiography (CTA) remains labor-intensive, time-consuming, and subject to interobserver variability. Automated computer-aided detection (CAD) systems may help standardize and accelerate this process. This study aimed to develop and evaluate a proof-of-concept automated CAD pipeline for CTA-based perforator mapping in DIEP flap planning.

Methods: A retrospective dataset of 504 CTA scans acquired for DIEP flap planning was analyzed. Fifty-five scans were manually annotated for perforator segmentation, and 100 scans were annotated for umbilicus landmark detection. A dual maximum intensity projection (MIP) depth-aware annotation workflow was introduced to standardize vessel labeling. The automated pipeline combined anatomical region of interest (ROI) localization with deep-learning-based vessel segmentation using a 3D Swin UNETR (Swin Transformer-based) model. Performance was evaluated using the Dice similarity coefficient (Dice), centerline Dice, recall, and the 95th percentile Hausdorff distance (HD95).

Results: Depth-aware annotation reduced labeling time by approximately 60%-70%. ROI localization was successful in all scans (28 ± 5 s), and umbilicus localization achieved an error of approximately 2 mm. The Swin UNETR model achieved a median Dice score of 0.58, outperforming Attention U-Net. Continuity-aware training improved Dice to 0.60 and recall to 0.58, while multiclass segmentation improved performance in adipose tissue.

Conclusion: This study demonstrates the feasibility of an automated CAD pipeline integrating standardized annotation, anatomical ROI localization, and deep-learning-based vessel segmentation for DIEP flap planning. This represents an important step toward faster, more reproducible, and clinically scalable CTA-based perforator mapping.