Regional SUV quantification in hybrid PET/MR, a comparison of two atlas-based automatic brain segmentation methods

Abstract

Background: Quantitative analysis of brain positron-emission tomography (PET) depends on structural segmentation, which can be time-consuming and operator-dependent when performed manually. Previous automatic segmentation usually registered subjects' images onto an atlas template (defined as RSIAT here) for group analysis, which changed the individuals' images and probably affected regional PET segmentation. In contrast, we could register atlas template to subjects' images (RATSI), which created an individual atlas template and may be more accurate for PET segmentation. We segmented two representative brain areas in twenty Parkinson disease (PD) and eight multiple system atrophy (MSA) patients performed in hybrid positron-emission tomography/magnetic resonance imaging (PET/MR). The segmentation accuracy was evaluated using the Dice coefficient (DC) and Hausdorff distance (HD), and the standardized uptake value (SUV) measurements of these two automatic segmentation methods were compared, using manual segmentation as a reference.

Results: The DC of RATSI increased, and the HD decreased significantly (P < 0.05) compared with the RSIAT in PD, while the results of one-way analysis of variance (ANOVA) found no significant differences in the SUV_mean and SUV_max among the two automatic and the manual segmentation methods. Further, RATSI was used to compare regional differences in cerebral metabolism pattern between PD and MSA patients. The SUV_mean in the segmented cerebellar gray matter for the MSA group was significantly lower compared with the PD group (P < 0.05), which is consistent with previous reports.

Conclusion: The RATSI was more accurate for the caudate nucleus and putamen automatic segmentation and can be used for regional PET analysis in hybrid PET/MR.

Keywords: Atlas-based; Multiple system atrophy; PET/MR; Parkinson disease; Segmentation.

Fig. 1

The brain atlas template, which segments the brain into 70 regions, labeled with numbers from 1 to 70 and showed with different colors. It was used for the following automatic segmentations

Fig. 2

The diagram displaying the processing steps of the two atlas-based automatic methods for whole brain automatic segmentation and regional ¹⁸F-FDG PET quantification

Fig. 3

The boxplots displaying distributions of the Dice coefficient (a) and Hausdorff distance (b), which were used to evaluate the brain segmentation accuracy in compared with the ground truth, the manual segmentation results. The red and green boxplots represented the results of RSIAT method and RATSI, respectively. Representative nuclei including caudate nucleus (left: CAU_L, right: CAU_R) and putamen (left: PUT_L, right: PUT_R) were segmented for analysis

Fig. 4

Representative visualization of segmented brain nuclei including caudate nucleus (green: CAU_L, red: CAU_R) and putamen (blue: PUT_L, yellow: PUT_R) by using manual segmentation (b), RSIAT method (c), and RATSI (d). The areas in the white box in T₁ images (a) were the regions shown below. And the segmented regions of interest (ROIs) for nuclei were overlapped on T₁ images in coronal (left), sagittal (middle), and axial views (right), respectively, for better visualization

Fig. 5

The boxplots displaying the distribution of the SUV_mean (a) and SUV_max (b) in caudate nucleus (left: CAU_L, right: CAU_R) and putamen (left: PUT_L, right: PUT_R) from twenty PD patients with manual segmentation (red), the RSIAT method (green), and RATSI (blue)

Fig. 6

Bland–Altman graphs to evaluate the SUV_mean consistency of the RSIAT method (a) and RATSI (b) in comparison with the manual segmentation in the four representative regions including the left, right caudate nucleus, and putamen. The SD represents the standard deviation