Probabilistic segmentation of white lesions in MR imaging

被引:271
作者
Anbeek, P [1 ]
Vincken, KL [1 ]
van Osch, MJP [1 ]
Bisschops, RHC [1 ]
van der Grond, J [1 ]
机构
[1] Univ Utrecht, Med Ctr, Dept Radiol, Image Sci Inst, NL-3584 CX Utrecht, Netherlands
关键词
segmentation; white matter lesions; multiple sclerosis;
D O I
10.1016/j.neuroimage.2003.10.012
中图分类号
Q189 [神经科学];
学科分类号
071006 [神经生物学];
摘要
A new method has been developed for fully automated segmentation of white matter lesions (WMLs) in cranial MR imaging. The algorithm uses information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It is based on the K-Nearest Neighbor (KNN) classification technique that builds a feature space from voxel intensities and spatial information. The technique generates images representing the probability per voxel being part of a WML. By application of thresholds on these probability maps, binary segmentations can be obtained. ROC curves show that the segmentations achieve both high sensitivity and specificity. A similarity index (SI), overlap fraction (OF) and extra fraction (EF) are calculated for additional quantitative analysis of the result. The SI is also used for determination of the optimal probability threshold for generation of the binary segmentation. Using probabilistic equivalents of the SI, OF and EF, the probability maps can be evaluated directly, providing a powerful tool for comparison of different classification results. This method for automated WML segmentation reaches an accuracy that is comparable to methods for multiple sclerosis (MS) lesion segmentation and is suitable for detection of WMLs in large and longitudinal population studies. (C) 2004 Elsevier Inc. All rights reserved.
引用
收藏
页码:1037 / 1044
页数:8
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