Improving the Segmentation of Therapy-induced Leukoencephalopathy in Children with Acute Lymphoblastic Leukemia Using a Priori Information and a Gradient Magnitude Threshold

Reliably quantifying therapy-induced leukoencephalopathy is a challenging task due to the similarity between its MR properties and those of normal tissues. Multispectral MR images were analyzed for 15 children treated for acute lymphoblastic leukemia. Three different analysis techniques were compared to examine improvements in the segmentation accuracy of leukoencephalopathy versus manual tracings by two experienced observers. The original technique used a white matter mask based on the segmentation of the first serial examination of each patient and no a priori information. The modified techniques combine spatially normalized a priori maps as input and a gradient magnitude threshold. The second technique used a 2D threshold, while the third algorithm utilized a 3D threshold. MR images were segmented with a Kohonen self-organizing map for all three algorithms. Kappa values were compared for the three techniques to each observer and statistically significant improvements were seen between the original and third algorithms (Observer 1: 0.651, 0.744, P = 0.015; Observer 2: 0.603, 0.699, P = 0.024). More accurate and reliable quantification reduces the amount of variance in MR measures and facilitates clinical trials to determine the clinical significance of leukoencephalopathy in this vulnerable population.