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Related Experiment Video

Updated: Sep 15, 2025

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

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

Published on: November 30, 2022

¹⁸F-FDG PET-based liver segmentation using deep-learning.

Yuta Kaneko^1,2, Kenta Miwa^3,4, Tensho Yamao^2,5

¹Department of Radiology, Fukushima Medical University Hospital, 1 Hikarigaoka, Fukushima, Fukushima, 960-1247, Japan.

Physical and Engineering Sciences in Medicine

|July 15, 2025

Summary

This study developed a deep learning method for liver segmentation using only 18F-FDG PET scans. This approach achieves high accuracy, enabling efficient liver uptake evaluation without CT or MRI.

Keywords:

18F-FDG Deep-Learning PET Segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Liver segmentation traditionally relies on CT/MRI, facing alignment and artifact issues.
Deep learning (DL) segmentation using 18F-FDG PET alone is underexplored.
Accurate liver segmentation is crucial for assessing metabolic activity and treatment response.

Purpose of the Study:

To develop and validate a DL model for segmenting the entire liver exclusively from 18F-FDG PET images.
To overcome limitations of multi-modal imaging in liver segmentation.
To enable rapid and stable evaluation of liver uptake from PET data.

Main Methods:

Utilized a 3D U-Net architecture from nnUNet for segmentation.
Trained and validated the model on 120 patient 18F-FDG PET datasets using 5-fold cross-validation.
Evaluated segmentation accuracy with Intersection over Union (IoU) and Dice coefficient, and image quality with SUVmean, SUVmax, and SNR.

Main Results:

Achieved high segmentation accuracy with an average IoU of 0.89 and Dice coefficient of 0.94 on the test set.
Demonstrated no significant discrepancies in image quality metrics compared to ground truth.
Successfully extracted liver regions from 18F-FDG PET images, enabling accurate uptake assessment.

Conclusions:

A DL model can accurately segment the liver using only 18F-FDG PET images.
This method offers a viable alternative to CT/MRI-based segmentation, reducing artifacts and alignment issues.
The approach facilitates efficient and reliable evaluation of liver metabolic activity in clinical settings.