Machine-learning based automated segmentation tool development for large-scale multicenter MRI data analysis

Eun Young Kim

doi:10.17077/etd.u5b4jszi

Background: Volumetric analysis of brain structures from structural Mag- netic Resonance (MR) images advances the understanding of the brain by providing means to study brain morphometric changes quantitatively along aging, development, and disease status. Due to the recent increased emphasis on large-scale multicenter brain MR study design, the demand for an automated brain MRI processing tool has increased as well. This dissertation describes an automatic segmentation framework for subcortical structures of brain MRI that is robust for a wide variety of MR data. Method: The proposed segmentation framework, BRAINSCut, is an inte- gration of robust data standardization techniques and machine-learning approaches. First, a robust multi-modal pre-processing tool for automated registration, bias cor- rection, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. The segmentation framework was then constructed to achieve robustness for large-scale data via the following comparative experiments: 1) Find the best machine-learning algorithm among several available approaches in the field. 2) Find an efficient intensity normalization technique for the proposed region-specific localized normalization with a choice of robust statistics. 3) Find high quality features that best characterize the MR brain subcortical structures. Our tool is built upon 32 handpicked multi-modal muticenter MR images with man- ual traces of six subcortical structures (nucleus accumben, caudate nucleus, globus pallidum, putamen, thalamus, and hippocampus) from three experts. A fundamental task associated with brain MR image segmentation for re- search and clinical trials is the validation of segmentation accuracy. This dissertation evaluated the proposed segmentation framework in terms of validity and reliability. Three groups of data were employed for the various evaluation aspects: 1) traveling human phantom data for the multicenter reliability, 2) a set of repeated scans for the measurement stability across various disease statuses, and 3) a large-scale data from Huntington's disease (HD) study for software robustness as well as segmentation accuracy. Result: Segmentation accuracy of six subcortical structures was improved with 1) the bias-corrected inputs, 2) the two region-specific intensity normalization strategies and 3) the random forest machine-learning algorithm with the selected feature-enhanced image. The analysis of traveling human phantom data showed no center-specific bias in volume measurements from BRAINSCut. The repeated mea- sure reliability of the most of structures also displayed no specific association to disease progression except for caudate nucleus from the group of high risk for HD. The constructed segmentation framework was successfully applied on multicenter MR data from PREDICT-HD [133] study ( < 10% failure rate over 3000 scan sessions pro- cessed). Conclusion: Random-forest based segmentation method is effective and robust to large-scale multicenter data variation, especially with a proper choice of the intensity normalization techniques. Benefits of proper normalization approaches are more apparent compared to the custom set of feature-enhanced images for the ccuracy and robustness of the segmentation tool. BRAINSCut effectively produced subcortical volumetric measurements that are robust to center and disease status with validity confirmed by human experts and low failure rate from large-scale multicenter MR data. Sample size estimation, which is crutial for designing efficient clinical and research trials, is provided based on our experiments for six subcortical structures.

Machine-learning based automated segmentation tool development for large-scale multicenter MRI data analysis

Files and links (1)

Abstract

Details

Metrics