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Plexiform neurofibromas (PNs) are a major manifestation of neurofibromatosis-1 (NF1), a common genetic disease involving the nervous system. Treatment decisions are mostly based on a gross assessment of changes in tumor using MRI. Accurate volumetric measurements are rarely performed in this kind of tumors mainly due to its great dispersion, size, and multiple locations. This paper presents a semi-automatic method for segmentation of PN from STIR MRI scans. The method starts with a user-based delineation of the tumor area in a single slice and automatically segments the PN lesions in the entire image based on the tumor connectivity. Experimental results on seven datasets, with lesion volumes in the range of 75-690 ml, yielded a mean absolute volume error of 10 % (after manual adjustment) as compared to manual segmentation by an expert radiologist. The mean computation and interaction time was 13 versus 63 min for manual annotation.

Original publication




Journal article


Med Biol Eng Comput

Publication Date





877 - 884


Algorithms, Artificial Intelligence, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Neurofibroma, Plexiform, Observer Variation, Pattern Recognition, Automated, Pilot Projects, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface