Probabilistic Brain Tissue Segmentation in Neonatal Magnetic Resonance Imaging

Petronella Anbeek, Koen L Vincken, Floris Groenendaal, Annemieke Koeman, Matthias J P van Osch, Jeroen Van der Grond
2008 Pediatric Research  
A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal fluid (CSF). The segmentation algorithm is based on information from T2-weighted (T2-w) and inversion recovery (IR) scans. The method uses a K nearest neighbor (KNN) classification technique with features derived from spatial information and voxel intensities. Probabilistic segmentations of
more » ... ach tissue type were generated. By applying thresholds on these probability maps, binary segmentations were obtained. These final segmentations were evaluated by comparison with a gold standard. The sensitivity, specificity, and Dice similarity index (SI) were calculated for quantitative validation of the results. High sensitivity and specificity with respect to the gold standard were reached: sensitivity Ͼ0.82 and specificity Ͼ0.9 for all tissue types. Tissue volumes were calculated from the binary and probabilistic segmentations. The probabilistic segmentation volumes of all tissue types accurately estimated the gold standard volumes. The KNN approach offers valuable ways for neonatal brain segmentation. The probabilistic outcomes provide a useful tool for accurate volume measurements. The described method is based on routine diagnostic magnetic resonance imaging (MRI) and is suitable for large population studies.
doi:10.1203/pdr.0b013e31815ed071 pmid:18091357 fatcat:6pqf6qno35hjpp4eqyu4rqrxjy