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Assessing quantitative performance and expert review of multiple deep learning-based frameworks for computed tomography-based abdominal organ auto-segmentation

Udbhav S.Ram Joel A.Pogue Michael Soike Neil T.Pfister Rojymon Jacob Carlos E.Cardenas

Intelligent Oncology2025，Vol.1Issue(2)：P.160-171,12.

Intelligent Oncology2025，Vol.1Issue(2)：P.160-171,12.DOI:10.1016/j.intonc.2025.03.003

Assessing quantitative performance and expert review of multiple deep learning-based frameworks for computed tomography-based abdominal organ auto-segmentation

Udbhav S.Ram ¹Joel A.Pogue ¹Michael Soike ¹Neil T.Pfister ¹Rojymon Jacob ¹Carlos E.Cardenas¹

作者信息

1. Department of Radiation Oncology,The University of Alabama at Birmingham,Birmingham AL 35233,United States
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摘要

关键词

AutoML/Segmentation/Radiation oncology/nnU-Net/Auto3DSeg

分类

医药卫生

引用本文复制引用

Udbhav S.Ram,Joel A.Pogue,Michael Soike,Neil T.Pfister,Rojymon Jacob,Carlos E.Cardenas..Assessing quantitative performance and expert review of multiple deep learning-based frameworks for computed tomography-based abdominal organ auto-segmentation[J].Intelligent Oncology,2025,1(2):P.160-171,12.

基金项目

funding from the University of Alabama at Birmingham,the National Institutions of Health/National Cancer Institute Award(LRP0000018407) （LRP0000018407）

National Center for Advancing Translational Sciences(5KL2TR003097-05). （5KL2TR003097-05）

Intelligent Oncology

ISSN：2950-2616

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