Imputation Strategy for Reliable Regional MRI Morphological Measurements

Area of Science:

• Neuroimaging
• Computational Anatomy
• Medical Image Analysis

Background:

• Regional morphological analysis is vital in neuroimaging.
• Brain segmentation results often contain variability due to suboptimal segmentation.
• Manual correction of segmentation errors is time-consuming and costly for large studies.

Purpose of the Study:

• To develop a computational approach for correcting segmentation errors using machine learning imputation.
• To reduce the time and cost associated with manual correction of brain segmentation outputs.
• To derive accurate morphological measures by treating segmentation correction as a missing data problem.

Main Methods:

• Framed segmentation correction as a missing data problem.
• Utilized machine learning imputation, specifically a random forest technique, to replace incorrect segmentation values.
• Evaluated the approach on a cohort of 970 subjects with simulated segmentation errors.

Main Results:

• The random forest imputation technique achieved high accuracy (r=0.93, p<0.0001) in recovering gold standard results when 30% of segmentations were incorrect.
• Demonstrated the effectiveness of the imputation method in a large cohort study.
• Identified the random forest technique as most effective for big data studies (N>250).

Conclusions:

• Machine learning imputation offers an accurate and efficient alternative to manual correction of brain segmentation errors.
• This computational approach significantly reduces the burden of post-segmentation correction in large-scale neuroimaging research.
• The proposed method is particularly beneficial for big data studies, enhancing the feasibility and reliability of morphological analysis.