Deep Learning Vessel Segmentation for Microsurgical Free Tissue Transfer

Introduction: Free autologous tissue techniques like DIEP (deep inferior epigastric perforator) are regarded as state-of-the-art for patients undergoing breast reconstruction after oncological mastectomy. However, surgeons have to rely on their experience in the identification of the vascular perforators suitable for flap harvest as there exists no standardized approach to this problem.
Objective: To develop a deep-learning method to autonomously segment vessels in the abdominal wall of patients undergoing autologous breast reconstruction to guide pre-surgical planning.
Methods: Manual segmentation of vessel perforators was performed on abdominal CTAs of 24 patients (20 training, 4 validation) undergoing autologous breast reconstruction at the Brigham and Women’s Hospital by an expert rater. A 3D U-net model implemented in DeepNeuro with Keras backend (Beers et al. 2018) was trained to automatically segment vessels in CTAs.
Results: Sensitivity and specificity of the trained model was 0.70/0.70 (training/validation) and 0.99/0.98 (training/validation), respectively.
Conclusion: The described model can reliably perform automatic vessel segmentation in CTA. We will further evaluate this result for guidance of pre-surgical decision making.