A new algorithm is presented for the automatic segmentation and classi\ufb01cation of brain tissue from 3D MR scans. It uses discriminative Random Decision Forest classi\ufb01cation and takes into account partial volume e\ufb00ects. This is combined with correction of intensities for the MR bias \ufb01eld, in conjunction with a learned model of spatial context, to achieve accurate voxel-wise classi\ufb01cation. Our quantitative validation, carried out on existing labelled datasets, demonstrates improved results over the state of the art, especially for the cerebro-spinal \ufb02uid class which is the most di\ufb03cult to label accurately<\/p>\n","protected":false},"excerpt":{"rendered":"
A new algorithm is presented for the automatic segmentation and classi\ufb01cation of brain tissue from 3D MR scans. It uses discriminative Random Decision Forest classi\ufb01cation and takes into account partial volume e\ufb00ects. 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