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01-12-2022 | Magnetic Resonance Imaging | Research

Deep learning-based automated segmentation of eight brain anatomical regions using head CT images in PET/CT

Authors: Tong Wang, Haiqun Xing, Yige Li, Sicong Wang, Ling Liu, Fang Li, Hongli Jing

Published in: BMC Medical Imaging | Issue 1/2022

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Abstract

Objective

We aim to propose a deep learning-based method of automated segmentation of eight brain anatomical regions in head computed tomography (CT) images obtained during positron emission tomography/computed tomography (PET/CT) scans. The brain regions include basal ganglia, cerebellum, hemisphere, and hippocampus, all split into left and right.

Materials and methods

We enrolled patients who underwent both PET/CT imaging (with an extra head CT scan) and magnetic resonance imaging (MRI). The segmentation of eight brain regions in CT was achieved by using convolutional neural networks (CNNs): DenseVNet and 3D U-Net. The same segmentation task in MRI was performed by using BrainSuite13, which was a public atlas label method. The mean Dice scores were used to assess the performance of the CNNs. Then, the agreement and correlation of the volumes of the eight segmented brain regions between CT and MRI methods were analyzed.

Results

18 patients were enrolled. Four of the eight brain regions obtained high mean Dice scores (> 0.90): left (0.978) and right (0.912) basal ganglia and left (0.945) and right (0.960) hemisphere. Regarding the agreement and correlation of the brain region volumes between two methods, moderate agreements were observed on the left (ICC: 0.618, 95% CI 0.242, 0.835) and right (ICC: 0.654, 95% CI 0.298, 0.853) hemisphere. Poor agreements were observed on the other regions. A moderate correlation was observed on the right hemisphere (Spearman’s rho 0.68, p = 0.0019). Lower correlations were observed on the other regions.

Conclusions

The proposed deep learning-based method performed automated segmentation of eight brain anatomical regions on head CT imaging in PET/CT. Some regions obtained high mean Dice scores and the agreement and correlation results of the segmented region volumes between two methods were moderate to poor.
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Metadata
Title
Deep learning-based automated segmentation of eight brain anatomical regions using head CT images in PET/CT
Authors
Tong Wang
Haiqun Xing
Yige Li
Sicong Wang
Ling Liu
Fang Li
Hongli Jing
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2022
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-022-00807-4

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