Auto-segmentation of structures in cervical cancer treatment planning using a deep learning framework

Authors

Sarah M. Chacko, The University of Texas MD Anderson Cancer CenterFollow
Carlos Sjogreen PhD, The University of Texas MD Anderson Cancer CenterFollow
Jaganathan A. Parameshwaran MD, The University of Texas MD Anderson Cancer CenterFollow
Laurence E. Court PhD, The University of Texas MD Anderson Cancer CenterFollow
Tucker J. Netherton PhD, DMP, The University of Texas MD Anderson Cancer CenterFollow

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Description

In this study, we explore whether the streamlined PocketNet architecture can perform comparably to the more robust nnU-Net framework for the auto-segmentation of pelvic structures and tumor volumes in the CT scans of cervical cancer patients.

DOI

https://doi.org/10.52519/00151

Publication Date

8-2024

Recommended Citation

Chacko, Sarah M.; Sjogreen, Carlos PhD; Parameshwaran, Jaganathan A. MD; Court, Laurence E. PhD; and Netherton, Tucker J. PhD, DMP, "Auto-segmentation of structures in cervical cancer treatment planning using a deep learning framework" (2024). Students Summer in Oncology at Anderson Research (SOAR). 5.
doi:https://doi.org/10.52519/00151