Abstract
Regional morphological analysis represents a crucial step in most neuroimaging studies. Results from brain segmentation techniques are intrinsically prone to certain degrees of variability, mainly as results of suboptimal segmentation. To reduce this inherent variability, the errors are often identified through visual inspection and then corrected (semi)manually. Identification and correction of incorrect segmentation could be very expensive for large-scale studies. While identification of the incorrect results can be done relatively fast even with manual inspection, the correction step is extremely time-consuming, as it requires training staff to perform laborious manual corrections. Here we frame the correction phase of this problem as a missing data problem. Instead of manually adjusting the segmentation outputs, our computational approach aims to derive accurate morphological measures by machine learning imputation. Data imputation techniques may be used to replace missing or incorrect region average values with carefully chosen imputed values, all of which are computed based on other available multivariate information. We examined our approach of correcting segmentation outputs on a cohort of 970 subjects, which were undergone an extensive, time-consuming, manual post-segmentation correction. A random forest imputation technique recovered the gold standard results with a significant accuracy (r = 0.93, p < 0.0001; when 30% of the segmentations were considered incorrect in a non-random fashion). The random forest technique proved to be most effective for big data studies (N > 250).
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Acknowledgements
This work was supported by the National Institute of Biomedical Imaging and Bioengineering (P41EB015922 and U54 EB020406), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R00HD065832), the National Institute of Mental Health (R01MH094343; K01MH1087610), National Institute of Diabetes and Digestive and Kidney Diseases (P30DK089503), National Institute of Neurological Disorders and Stroke (P30DK089503), National Institute of Nursing Research (P20 NR015331). This work was partially supported by NSF grants 1734853, 1636840, 1416953, 0716055 and 1023115. Many colleagues, who are part of the Big Data Discovery Science (BDDS) community, contributed indirectly to this research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIBIB, NICHD, NIMH, NIDDK, NINDS, NINR or NIH.
This study was conducted as part of the “Big Data Discovery and Diversity through Research Education Advancement and Partnerships (BD3-REAP)” Project funded by National Institutes of Health (NIH)-R25; Grant number is IR25MD010397-01. Data collection and sharing for this project was funded by the Philadelphia Neurodevelopmental Cohort (PNC) and the Pediatric Imaging, Neurocognition and Genetics Study (PING) (National Institutes of Health Grant RC2DA029475).
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Sta. Cruz, S., Dinov, I.D., Herting, M.M. et al. Imputation Strategy for Reliable Regional MRI Morphological Measurements. Neuroinform 18, 59–70 (2020). https://doi.org/10.1007/s12021-019-09426-x
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DOI: https://doi.org/10.1007/s12021-019-09426-x