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International Journal of Computer Assisted Radiology and Surgery

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02.11.2018 | Original Article

Dual-modality multi-atlas segmentation of torso organs from [¹⁸F]FDG-PET/CT images

verfasst von: Hongkai Wang, Nan Zhang, Li Huo, Bin Zhang

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 3/2019

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Abstract

Purpose

Automated segmentation of torso organs from positron emission tomography/computed tomography (PET/CT) images is a prerequisite step for nuclear medicine image analysis. However, accurate organ segmentation from clinical PET/CT is challenging due to the poor soft tissue contrast in the low-dose CT image and the low spatial resolution of the PET image. To overcome these challenges, we developed a multi-atlas segmentation (MAS) framework for torso organ segmentation from 2-deoxy-2-[¹⁸F]fluoro-d-glucose PET/CT images.

Method

Our key idea is to use PET information to compensate for the imperfect CT contrast and use surface-based atlas fusion to overcome the low PET resolution. First, all the organs are segmented from CT using a conventional MAS method, and then the abdomen region of the PET image is automatically cropped. Focusing on the cropped PET image, a refined MAS segmentation of the abdominal organs is performed, using a surface-based atlas fusion approach to reach subvoxel accuracy.

Results

This method was validated based on 69 PET/CT images. The Dice coefficients of the target organs were between 0.80 and 0.96, and the average surface distances were between 1.58 and 2.44 mm. Compared to the CT-based segmentation, the PET-based segmentation gained a Dice increase of 0.06 and an ASD decrease of 0.38 mm. The surface-based atlas fusion leads to significant accuracy improvement for the liver and kidneys and saved ~ 10 min computation time compared to volumetric atlas fusion.

Conclusions

The presented method achieves better segmentation accuracy than conventional MAS method within acceptable computation time for clinical applications.

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Titel: Dual-modality multi-atlas segmentation of torso organs from [18F]FDG-PET/CT images
verfasst von: Hongkai Wang
Nan Zhang
Li Huo
Bin Zhang
Publikationsdatum: 02.11.2018
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 3/2019
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-018-1879-3

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Dual-modality multi-atlas segmentation of torso organs from [¹⁸F]FDG-PET/CT images

Abstract

Purpose

Method

Results

Conclusions

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