Automatic segmentation of the preterm neonatal brain with MRI using supervised classification

Sabina M. Chiţă; Manon Benders; Pim Moeskops; Karina J. Kersbergen; Max A. Viergever; Ivana Išgum

doi:10.1117/12.2006753

13 March 2013 Automatic segmentation of the preterm neonatal brain with MRI using supervised classification

Sabina M. Chiţă, Manon Benders, Pim Moeskops, Karina J. Kersbergen, Max A. Viergever, Ivana Išgum

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86693X (2013) https://doi.org/10.1117/12.2006753
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Cortical folding ensues around 13-14 weeks gestational age and a qualitative analysis of the cortex around this period is required to observe and better understand the folds arousal. A quantitative assessment of cortical folding can be based on the cortical surface area, extracted from segmentations of unmyelinated white matter (UWM), cortical grey matter (CoGM) and cerebrospinal fluid in the extracerebral space (CSF). This work presents a method for automatic segmentation of these tissue types in preterm infants. A set of T1- and T2-weighted images of ten infants scanned at 30 weeks postmenstrual age was used. The reference standard was obtained by manual expert segmentation. The method employs supervised pixel classification in three subsequent stages. The classification is performed based on the set of spatial and texture features. Segmentation results are evaluated in terms of Dice coefficient (DC), Hausdorff distance (HD), and modified Hausdorff distance (MHD) defined as 95th percentile of the HD. The method achieved average DC of 0.94 for UWM, 0.73 for CoGM and 0.86 for CSF. The average HD and MHD were 6.89 mm and 0.34 mm for UWM, 6.49 mm and 0.82 mm for CoGM, and 7.09 mm and 0.79 mm for CSF, respectively. The presented method can provide volumetric measurements of the segmented tissues, and it enables quantification of cortical characteristics. Therefore, the method provides a basis for evaluation of clinical relevance of these biomarkers in the given population.

Citation Download Citation

Sabina M. Chiţă, Manon Benders, Pim Moeskops, Karina J. Kersbergen, Max A. Viergever, and Ivana Išgum "Automatic segmentation of the preterm neonatal brain with MRI using supervised classification", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86693X (13 March 2013); https://doi.org/10.1117/12.2006753

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