Improving the segmentation of therapy-induced leukoencephalopathy using apriori information and a gradient magnitude threshold

摘要

Reliably quantifying therapy-induced leukoencephalopathy in children treated for cancer is a challenging task due to its varying MR properties and similarity to normal tissues and imaging artifacts. T1, T2, PD, and FLAIR images were analyzed for a subset of 15 children from an institutional protocol for the treatment of acute lymphoblastic leukemia. Three different analysis techniques were compared to examine improvements in the segmentation accuracy of leukoencephalopathy versus manual tracings by two expert observers. The first technique utilized no apriori information and a white matter mask based on the segmentation of the first serial examination of each patient. MR images were then segmented with a Kohonen Self-Organizing Map. The other two techniques combine apriori maps from the ICBM atlas spatially normalized to each patient and resliced using SPM99 software. The apriori maps were included as input and a gradient magnitude threshold calculated on the FLAIR images was also utilized. The second technique used a 2-dimensional threshold, while the third algorithm utilized a 3-dimensional threshold. Kappa values were compared for the three techniques to each observer, and improvements were seen with each addition to the original algorithm (Observer 1: 0.651, 0.653, 0.744; Observer 2: 0.603, 0.615, 0.699).