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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Automated liver and spleen segmentation for MR elastography maps using U-Nets.

Noah Jaitner¹, Jakob Ludwig¹, Tom Meyer¹

¹Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.

Scientific Reports

|March 29, 2025

Summary

This summary is machine-generated.

Automated liver and spleen segmentation using U-Nets in multifrequency magnetic resonance elastography (MRE) magnitude images enables accurate shear wave speed (SWS) quantification. Both pretrained and trained U-Nets demonstrated excellent performance, comparable to manual segmentation.

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Biophysics

Background:

Magnetic Resonance Elastography (MRE) is a non-invasive technique to assess tissue stiffness.
Accurate segmentation of organs like the liver and spleen is crucial for quantifying shear wave speed (SWS).
Manual segmentation is time-consuming and prone to inter-observer variability.

Purpose of the Study:

To compare pretrained and trained 2D and 3D U-Net models for automated liver and spleen segmentation in multifrequency MRE magnitude images.
To evaluate the feasibility of automated quantification of SWS using these segmentation models.
To assess the performance of U-Nets against manual segmentation using correlation, ICC, and Dice scores.

Main Methods:

Seventy-two healthy participants underwent multifrequency MRE at 1.5T or 3T.
Liver and spleen volumes of interest (VOIs) were segmented manually (ground truth) and automatically using pretrained and trained 2D and 3D U-Nets on MRE magnitude images.
Performance was evaluated using correlation analysis, intraclass correlation coefficients (ICCs), and Dice scores for both VOI and SWS values.

Main Results:

No statistically significant differences were found between ground truth and U-Net segmentations for VOI and SWS values (p ≥ 0.95).
Strong positive correlations (R=0.99 for liver, R=0.81-0.84 for spleen) and excellent agreement (ICC=0.99 for liver, ICC=0.90-0.92 for spleen) were observed.
2D U-Net achieved slightly higher Dice scores (liver: 0.95, spleen: 0.90), indicating excellent segmentation performance with minimal differences between U-Net models.

Conclusions:

Automated liver and spleen segmentation using 2D and 3D U-Nets on MRE magnitude images is highly feasible and accurate.
Leveraging anatomical information in MRE magnitude images allows for fully automated quantification of MRE parameters.
This approach offers a promising solution for efficient and reliable SWS quantification in clinical settings.