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.

Introduction The current gold standard for preoperative planning of deep inferior epigastric artery perforator (DIEAP) flap breast reconstruction is assessment of perforator vessels on computed tomography angiography (CTA) or magnetic resonance angiography (MRA) images in two-dimensional (2D) planes on a 2D screen. This research investigates the potential benefit of a 3D model of blood vessels on a 2D screen or Virtual Reality (VR) environment in preoperative planning for DIEAP flap breast reconstruction, compared to conventional CTA on a 2D screen. Materials & Methods A qualitative study was conducted among six reconstructive plastic surgeons and four residents. Participants were asked to select the ideal perforator for unilateral DIEAP flap reconstruction in three viewing environments: conventional CTA in a 2D view, a 3D model in a 2D view and a 3D model in an immersive virtual reality (VR) view. Subsequently, a questionnaire on anatomical structure visibility assessing six perforator characteristics (perforator location in the flap, calibre, intramuscular course through the rectus abdominis, perforator origin, subcutaneous branching and connection to other vessels) was completed. Second, an adjusted Usefulness, Satisfaction and Ease of Use (USE) questionnaire on system usability was filled out.Results Ten participants completed the experiments. The overall score distribution for perforator visibility characteristics was equal or higher except for perforator calibre in both 3D model views compared to conventional CTA. In the USE questionnaire, the 3D model in 2D view reported the highest scores for satisfaction and ease of learning. 90% of participants considered a 3D model in VR view to be of added value to preoperative planning, 60% considered a 3D model in 2D to be of added value. Overall, 40% would currently opt for the 3D model in a 2D view and 10% for the 3D model in a VR view. Conclusion This study presents a perspective on the application of 3D models for preoperative planning in DIEAP flap breast reconstructions. There is an interest in use of a 3D model in a 2D view or an immersive VR environment to optimize preoperative planning. The visibility of important perforator characteristics received overall higher scores for the viewing environments with a 3D model, highlighting the potential benefit for 3D models and VR application for selection of ideal perforators in preoperative planning for DIEAP flap breast reconstructions.