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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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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Acknowledgements

This research is supported by the youth program of National Natural Science Fund of China No. 81401475, the general program of National Natural Science Fund of China No. 61571076, the general program of Liaoning Science and Technology Project No. 2015020040, the Since and Technology Star Project Fund of Dalian City No. 2016RQ019, the Xinghai Scholar Cultivating Funding of Dalian University of Technology (No. DUT14RC(3)066), the cultivating program of Major National Natural Science Fund of China No. 91546123, the National Key Research and Development Program No. 2016YFC0103101, 2016YFC0103102 and 2016YFC0106402, and the Fundamental Research Funds for Central Universities No. DUT15LN02 and No. DUT16RC(3)099.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning, China

    Hongkai Wang, Nan Zhang & Bin Zhang

  2. Department of Nuclear Medicine, Peking Union Medical College Hospital, Beijing, China

    Li Huo

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  1. Hongkai Wang
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Correspondence to Bin Zhang.

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Wang, H., Zhang, N., Huo, L. et al. Dual-modality multi-atlas segmentation of torso organs from [18F]FDG-PET/CT images. Int J CARS 14, 473–482 (2019). https://doi.org/10.1007/s11548-018-1879-3

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