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Related Experiment Video

Updated: Jun 3, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Deep-Learning-based Automatic Segmentation and Quantitative Measurement of Normal Spleen in Chinese Adults

Tian Tan1, Yaofeng Zhang2, Xiaodong Zhang1

  • 1Department of Radiology, Peking University First Hospital, No.8, Xishiku Street, Xicheng District, Beijing100034, China.

Current Medical Imaging
|June 2, 2026
PubMed
Summary

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This study developed an automated deep learning tool for spleen segmentation on CT scans in Chinese adults. The tool accurately measures spleen volume and attenuation, revealing sex- and age-specific patterns crucial for clinical interpretation.

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • The spleen is a vital immune organ, but its volume changes in various diseases.
  • Current methods for assessing splenic enlargement are often semi-quantitative and may miss subtle volume variations.
  • There is a limited understanding of normal splenic morphometrics, including age- and sex-related patterns, in Chinese adults.

Purpose of the Study:

  • To develop and validate a deep learning model for automated spleen segmentation on CT scans.
  • To establish population-specific reference values for splenic morphometrics (volume, attenuation, dimensions) in Chinese adults.
  • To investigate age- and sex-related variations in splenic morphometrics.

Main Methods:

  • A 3D V-Net model was developed for automated spleen segmentation.
Keywords:
Artificial Intelligence.Chinese adultsComputed tomography (CT)Deep learningQuantitative measurementSpleen segmentation

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  • The model was trained on 2,856 CT examinations from four public datasets.
  • The model was applied to 1,520 CT examinations of individuals with normal spleens for morphometric analysis.

    • Main Results:

    • The deep learning model achieved high accuracy in spleen segmentation (DSC 0.982).
    • Significant differences in spleen volume were observed between males and females, with distinct age-related patterns for each sex.
    • Splenic attenuation varied significantly by sex across different contrast phases.

    Conclusions:

    • A deep learning pipeline enables accurate and efficient automated spleen segmentation and morphometric analysis on CT.
    • Population-specific reference values for spleen morphometrics are essential due to observed ethnic differences.
    • This approach supports objective interpretation of spleen size and attenuation in clinical practice.