TY - JOUR AU - Saxena, Eshika PY - 2022/06/28 Y2 - 2024/03/28 TI - Deep Learning for Personalized Preoperative Planning of Microsurgical Free Tissue Transfers JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 11 SE - AAAI Undergraduate Consortium DO - 10.1609/aaai.v36i11.21706 UR - https://ojs.aaai.org/index.php/AAAI/article/view/21706 SP - 13140-13141 AB - Breast reconstruction surgery requires extensive planning, usually with a CT scan that helps surgeons identify which vessels are suitable for harvest. Currently, there is no quantitative method for preoperative planning. In this work, we successfully develop a Deep Learning algorithm to segment the vessels within the region of interest for breast reconstruction. Ultimately, this information will be used to determine the optimal reconstructive method (choice of vessels, extent of the free flap/harvested tissue) to reduce intra- and postoperative complication rates. ER -