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Table 5 A list features to be used for lesion-free outcome prediction

From: Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy

Categories Details of features
I. Fiber tract features I.1. Histogram analysis (0, 25, 50, 75 and 100-percentile) of T1, T2, DWI, ADC, ZT1, ZT2, ZDWI, ZADC signal values within each of the 28 major fiber bundles as defined in the JHU atlas [123]
I.2. Skewness (asymmetry), kurtosis (flatness), uniformity and randomness (entropy and standard deviations) of T1, T2, DWI, ADC, ZT1, ZT2, ZDWI, ZADC signal values in each brain structures
II. Regional anatomy features II.1. Histogram analysis (0, 25, 50, 75 and 100-percentile) of T1, T2, DWI, ADC, ZT1, ZT2, ZDWI, ZADC signal values within the brain and each of the 61 auto-segmented brain structures/regions
II.2. Skewness (asymmetry), kurtosis (flatness), uniformity and randomness (entropy) of T1, T2, DWI, ADC, ZT1, ZT2, ZDWI, ZADC signal values in the brain and 61 auto-segmented regions
II.3. Volume of the 61 auto-segmented structures/regions as measured in T1 image
II.4. Left/right asymmetry in features II.1–II.3