A lightweight deep learning model for automated segmentation of Gynecologic organs and cervical Tumors on T2-weighted magnetic resonance imaging.
- DOI
- 10.1016/j.phro.2026.101049
- Published
- 2026 Jul
- Container
- Physics and imaging in radiation oncology
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.phro.2026.101049,
title = {A lightweight deep learning model for automated segmentation of Gynecologic organs and cervical Tumors on T2-weighted magnetic resonance imaging.},
author = {Twam A and Jacobsen MC and Celaya AE and Glenn R and Wei P and Sun J and Lin L and Klopp A and Venkatesan AM and Fuentes D},
year = {2026},
journal = {Physics and imaging in radiation oncology},
doi = {10.1016/j.phro.2026.101049},
url = {https://doi.org/10.1016/j.phro.2026.101049}
}RIS
TY - JOUR TI - A lightweight deep learning model for automated segmentation of Gynecologic organs and cervical Tumors on T2-weighted magnetic resonance imaging. AU - Twam A AU - Jacobsen MC AU - Celaya AE AU - Glenn R AU - Wei P AU - Sun J AU - Lin L AU - Klopp A AU - Venkatesan AM AU - Fuentes D PY - 2026 JO - Physics and imaging in radiation oncology DO - 10.1016/j.phro.2026.101049 UR - https://doi.org/10.1016/j.phro.2026.101049 ER -
APA
A, T., MC, J., AE, C., R, G., P, W., J, S., L, L., A, K., AM, V., & D, F. (2026). A lightweight deep learning model for automated segmentation of Gynecologic organs and cervical Tumors on T2-weighted magnetic resonance imaging.. Physics and imaging in radiation oncology. https://doi.org/10.1016/j.phro.2026.101049
Source records
- pubmed · retrieved 2026-09-26T20:37:08.349Z