Automated lesion detection on MRI scans using combined unsupervised and supervised methods

BMC Medical Imaging

Table 3 The performance of the unsupervised component only and the performance of the proposed method (combining both unsupervised and supervised components)when using different features for supervised training

Unsupervised classification
		Accuracy	Precision	Recall	Dice
		0.981±0.012	0.665±0.183	0.671±0.140	0.663±0.161
Supervised classification
Inputs	Classifiers	Accuracy	Precision	Recall	Dice
T1 MRI	zero-order	0.952±0.015	0.150±0.313	0.402±0.203	0.202±0.302
	1st-order	0.957±0.014	0.162±0.306	0.389±0.241	0.214±0.301
	2nd-order	0.954±0.015	0.144±0.347	0.377±0.272	0.193±0.342
	All combined	0.956±0.014	0.187±0.291	0.421±0.198	0.258±0.291
Prob. maps	zero-order	0.981±0.012	0.696±0.178	0.637±0.146	0.665±0.164
of WM, GM	1st-order	0.981±0.012	0.702±0.177	0.621±0.157	0.659±0.166
external CSF, LPM	2nd-order	0.968±0.013	0.503±0.192	0.609±0.159	0.551±0.173
	All combined	0.983±0.012	0.781±0.144	0.681±0.142	0.728±0.112
T1 MRI & prob.	zero-order	0.983±0.011	0.747±0.151	0.646±0.145	0.681±0.143
maps of WM, GM,	1st-order	0.983±0.011	0.754±0.149	0.649±0.142	0.687±0.144
external CSF, LPM	2nd-order	0.970±0.012	0.526±0.170	0.641±0.152	0.547±0.160
	All combined	0.983±0.011	0.783±0.143	0.685±0.131	0.731±0.106

ISSN: 1471-2342