A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images

The developed DL model demonstrates accurate adrenal gland segmentation, and enables a comprehensive study of age-related adrenal gland volume variations.

Authors

Xiaojing Kan (ORCID: https://orcid.org/0000-0003-2197-1714)
Yinghui Ge
Yuwei Xia (ORCID: https://orcid.org/0000-0002-2248-2296)
Fangbin Liu (ORCID: https://orcid.org/0000-0002-3276-0501)
Tuo Zhang (ORCID: https://orcid.org/0000-0002-6075-3384)
Fanxing Meng
Mengyuan Xu (ORCID: https://orcid.org/0000-0002-1788-8267)
Yingtian Pan
Jin Cai

Institutions

Zhengzhou Central Hospital (CN)
United Imaging Healthcare (China) (CN)

Publication Details

Journal: BMC Medical Imaging
Published: 2025-05-01
DOI: https://doi.org/10.1186/s12880-025-01682-5
Citations: 2
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 2.72

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article

A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images

Xiaojing Kan, Yinghui Ge, Yuwei Xia, Fangbin Liu et al.

May 1, 2025

2 citations

BMC Medical Imaging

Radiomics and Machine Learning in Medical Imaging

2.72

article

A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images

Xiaojing Kan, Yinghui Ge, Yuwei Xia, Fangbin Liu, Tuo Zhang, Fanxing Meng, Mengyuan Xu, Yingtian Pan, Jin Cai

May 1, 2025article en

2 citations

Abstract

The developed DL model demonstrates accurate adrenal gland segmentation, and enables a comprehensive study of age-related adrenal gland volume variations.

BMC Medical ImagingVol. 25(1)

Zhengzhou Central Hospital (CN), United Imaging Healthcare (China) (CN)

Zero hunger

Openalex Percentile: Top 11%

Radiomics and Machine Learning in Medical Imaging

2.72

DOI Open Access PDF OpenAlex

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A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images — Xiaojing Kan, Yinghui Ge, et al. · BMC Medical Imaging (2025) | TGRS Research Map | TGRS