Deep Learning for Personalized Preoperative Planning of Microsurgical Free Tissue Transfers
Abstract
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.
Cite
Text
Saxena. "Deep Learning for Personalized Preoperative Planning of Microsurgical Free Tissue Transfers." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21706Markdown
[Saxena. "Deep Learning for Personalized Preoperative Planning of Microsurgical Free Tissue Transfers." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/saxena2022aaai-deep/) doi:10.1609/AAAI.V36I11.21706BibTeX
@inproceedings{saxena2022aaai-deep,
title = {{Deep Learning for Personalized Preoperative Planning of Microsurgical Free Tissue Transfers}},
author = {Saxena, Eshika},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {13140-13141},
doi = {10.1609/AAAI.V36I11.21706},
url = {https://mlanthology.org/aaai/2022/saxena2022aaai-deep/}
}