Dual-modality multi-atlas segmentation of torso organs from [18F]FDG-PET/CT images
发布时间：2019-03-13
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- 论文类型：
- 期刊论文
- 发表时间：
- 2019-03-01
- 发表刊物：
- International journal of computer assisted radiology and surgery
- 收录刊物：
- PubMed
- 文献类型：
- J
- 卷号：
- 14
- 期号：
- 3
- 页面范围：
- 473-482
- ISSN号：
- 1861-6429
- 关键字：
- Atlas fusion,Multi-atlas segmentation,Nuclear medicine image analysis,PET/CT
- 摘要：
- 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.
- 第一作者
- Wang Hongkai
- 合写作者
- Huo Li,Zhang Bin,Zhang Nan