Subject-Level Segmentation Precision Weights for Volumetric Studies Involving Label Fusion
Poster No:
1498
Submission Type:
Abstract Submission
Authors:
Christina Chen1, Sandhitsu Das1, M Tisdall1, Fengling Hu1, Andrew Chen2, Paul Yushkevich1, David Wolk1, Russell Shinohara1
Institutions:
1University of Pennsylvania, Philadelphia, PA, 2Medical University of South Caroline, Charleston, SC
First Author:
Co-Author(s):
Introduction:
Region-specific changes in brain volume accompany many neurological diseases. Delineating these changes can link structural and clinical findings to illuminate disease biology and facilitate clinical management. For example, Alzheimer's disease (AD) is a neurodegenerative disorder characterized by neuropathological deposits that concentrate in regions such as the hippocampus (DeTure, 2019; Serrano-Pozo, 2011). MRI volumetric studies have augmented these findings by establishing correlations between the magnitude of hippocampal atrophy and the severity of clinical disease (Jack, Jr., 2011). Deriving hippocampal volumes from a neuroimaging study entails segmenting the hippocampus in each image. Manual segmentation is cumbersome and prone to errors. Automated atlas-based segmentation borrows information from expert-labeled images (atlases). Multi-atlas label fusion refines single-atlas segmentation by aggregating labels from multiple atlases. One way of testing for group differences in hippocampal volume involves estimating the hippocampal volume for each subject and then regressing these estimates onto the group labels. However, this approach equalizes the contributions of all study subjects. Retaining poorly segmented images can attenuate power or induce bias, but discarding them altogether can potentially deplete the sample size. Ideally, we would prefer weighting each volume estimate according to its precision. We propose a novel, statistically motivated method of deriving subject-level precision weights by quantifying how resampling different sets of atlases perturbs each subject's volume estimate.
Methods:
We used T1-weighted MRIs from ADNI 2 and 3. In our first experiment, we compared 200 images from controls (CN) to 200 images from AD subjects. In our second experiment, we compared 200 images from amyloid-negative controls to 200 images from subjects with amyloid-positive early mild cognitive impairment (EMCI). For multi-atlas segmentation, we used the 35 OASIS (Marcus, 2010) atlases provided for Landman (2012). We fused the labels from the registered atlases via joint label fusion (JLF) (Wang, 2013). For each image, we obtained 300 bootstrapped atlas collections by sampling from our 35 atlases, derived the hippocampal volume from the JLF segmentation produced by each atlas collection, computed the variance across these 300 volume estimates, and computed the inverse of this variance to weight the hippocampal volume estimate for each subject. We validated our weights by assessing how incorporating them affects the type I error rate and power in detecting a disease status effect on hippocampal volume. To derive a p-value for this effect, we performed a weighted permutation test (Freedman, 1983). In all of our experiments, we regressed the subjects' hippocampal volume estimates onto their disease status (CN vs. AD or CN vs. EMCI), ages, and intracranial volumes.
Results:
Our weighting and permutation procedure controls the type I error rate. Moreover, it confers significantly increased power compared to OLS. For example, for 160 samples, weighting improves the power to detect a difference in mean hippocampal volume between controls and AD subjects from 0.495 to 0.736 (Fig. 1). The power gain for differentiating between amyloid-negative controls and amyloid-positive EMCI subjects is even more impressive. For example, for 160 samples, weighting improves the power from 0.143 to 0.836 (Fig. 2).
Conclusions:
We proposed a novel way of deriving subject-level weights that quantify the variability in volume estimates across joint segmentations produced by different atlas collections. We demonstrated on real data that incorporating these weights significantly improves power for detecting a mean group difference in hippocampal volume. Thus, our method provides definite guidance on how to leverage the uncertainty information extracted from the segmentation procedure to test hypotheses motivated by biological questions.
Modeling and Analysis Methods:
Image Registration and Computational Anatomy 2
Methods Development 1
Segmentation and Parcellation
Keywords:
Data analysis
Modeling
Segmentation
Statistical Methods
STRUCTURAL MRI
Other - neuroimaging,uncertainty
1|2Indicates the priority used for review
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Provide references using APA citation style.
Freedman, D. (1983). A nonstochastic interpretation of reported significance levels. Journal of Business and Economic Statistics, 1(4), 292-298.
Jack, Jr., C. R. (2011). Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer’s disease. Alzheimer’s and Dementia, 7(4), 474-485.
Landman, B. (2012). MICCAI 2012 workshop on multi-atlas labeling.
Marcus, D. S. (2010). Open access series of imaging studies (OASIS): Longitudinal MRI data in nondemented and demented older adults. Journal of Cognitive Neuroscience, 22(12), 2677-2684.
Serrano-Pozo, A. (2011). Neuropathological alterations in Alzheimer disease. Cold Spring Harbor Perspectives in Medicine, 1(1).
Wang, H. (2013). Multi-atlas segmentation with joint label fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3), 611-623.