Table 2:

Quantitative comparison of the segmentation prediction across different tissue feature descriptors and machine learning classifiers on test VERIO data. Reported are the prediction accuracy (ACC) across the entire brain, and separately across tissue boundary and non-boundary regions, as well as the probabilistic similarity index (PSI) for WM, GM and CSF.

	region	SVM	CNN	CNNAT
FDTI	entire brain	56.65±6.61%	76.25±3.66%	76.68±4.35%
	boundary	55.82±6.62%	66.22±4.21%	69.49±4.64%
	non-boundary	57.58±6.62%	88.78±3.56%	90.32±2.79%
FOrigDKI	entire brain	59.55±3.53%	77.78±3.10%	78.35±3.02%
	boundary	58.73±3.54%	66.19±3.67%	70.36±3.67%
	non-boundary	60.48±3.52%	91.53±2.14%	91.57±2.30%
FProposed	entire brain	62.28±3.49%	79.39±2.25%	80.30±2.02%
	boundary	61.46±3.48%	68.70±3.05%	73.00±2.78%
	non-boundary	63.21±3.48%	92.05±2.45%	92.62±2.55%
FDTI	GM	0.831±0.019	0.947±0.019	0.948±0.017
	WM	0.814±0.009	0.927±0.009	0.934±0.009
	CSF	0.831±0.031	0.936±0.030	0.940±0.029
FOrigDKI	GM	0.831±0.023	0.947±0.021	0.949±0.021
	WM	0.817±0.017	0.932±0.017	0.934±0.017
	CSF	0.858±0.015	0.961±0.013	0.956±0.015
FProposed	GM	0.832±0.023	0.946±0.021	0.951±0.021
	WM	0.836±0.011	0.949±0.011	0.953±0.010
	CSF	0.847±0.032	0.951 ±0.028	0.965±0.032