Table 4 A list features to be used for lesion-based outcome prediction

I. Lesion anatomy

I.1. Mass center in standard neonatal atlas space

I.2. Percentage of the whole-brain volume and the volume of each of the 61 auto-segmented brain structures being injured [76, 78, 122]

I.3. Ratios of volumetric injury in the same brain structures between the left and right hemisphere

I.4. Percentage and distribution of HIE lesions in 28 major fiber tracts as defined in the JHU atlas [123]

II. Lesion geometry

II.1. Lesion volume

II.2. Maximum diameter along different orthogonal directions, maximum surface of lesion, lesion compactness, lesion spherecity, surface-to-volume ratio

III. Lesion heterogeneity

III.1. Histogram analysis (0, 25, 50, 75 and 100-percentile) of T1, T2, DWI, ADC, Z_T1, Z_T2, Z_DWI, Z_ADC signal values within the lesion regions

III.2. Skewness (asymmetry), kurtosis (flatness), uniformity and randomness (entropy and standard deviations) of T1, T2, DWI, ADC, Z_T1, Z_T2, Z_DWI, Z_ADC signal values within the lesion regions

IV. Lesion texture

IV.1. gray-level co-occurrence matrix (GLCM) features and gray-level run-length matrix (GLRLM) of T1, T2, DWI, ADC, Z_T1, Z_T2, Z_DWI, Z_ADC signal values within lesion regions

IV.2. fractal analysis, Minkowski functionals, wavelet transform and Laplacian transforms of Gaussian-filtered images for the lesion regions