Indexed by:
Journal Papers
First Author:
Wang Hongkai
Co-author:
Zhang Nan,Huo Li,Zhang Bin
Date of Publication:
2019-03-01
Journal:
International journal of computer assisted radiology and surgery
Included Journals:
PubMed
Document Type:
J
Volume:
14
Issue:
3
Page Number:
473-482
ISSN No.:
1861-6429
Key Words:
Atlas fusion,Multi-atlas segmentation,Nuclear medicine image analysis,PET/CT
Abstract:
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.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.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.The presented method achieves better segmentation accuracy than conventional MAS method within acceptable computation time for clinical applications.
Translation or Not:
no