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) 2024. 5.
doi:https://doi.org/10.52519/00151