Introduction: Breast reconstruction after a mastectomy is crucial to improve a patient’s quality of life. The Deep Inferior Epigastric artery Perforator (DIEP) flap procedure is considered the golden standard for breast reconstruction. Three-dimensional (3D) visualization methods have shown promise in providing a better understanding of Computed Tomography Angiography (CTA) information and potentially reducing operative time. However, for these methods manual segmentation of the perforators is needed, which is time-consuming and prone to variability. Automated segmentation using Deep learning (DL) can potentially overcome these limitations and provide accurate and efficient segmentation of the DIEP flap perforators. This study aimed to evaluate the application of DL for automated perforator segmentation in DIEP flap breast reconstruction, improving the efficiency and objectivity of DIEP flap perforator segmentation. Materials and methods: The dataset comprised 25 CTA scans for training and validation, and 5 CTA scans for testing. DL was employed for automated segmentation, and quantitative evaluation included metrics such as Dice coefficient score, recall, precision, surface distance, and centerline overlap. The qualitative evaluation involved grading the clinical acceptability of segmentations by four experienced plastic reconstructive surgeons. Results: On the training set, a Dice score of 0.58 (± 0.08) and a true positive centerline overlap of 0.66 (± 0.10) were achieved for perforator segmentation. The DL model successfully segmented the intramuscular main branch, but some perforators were missed in the subcutaneous fat tissue. Combined grading by all surgeons showed no statistical difference between manual and automated segmentations and both segmentations were evaluated as clinically acceptable. Conclusion: Automated DL segmentation holds promise for enhancing the efficiency and objectivity of identifying DIEP flap perforators in CTA images, providing an alternative to manual segmentation. Nonetheless, further research is needed to refine the automated segmentation results and to validate the generalizability and clinical applicability of the DL segmentation approach in larger patient cohorts and different clinical settings.