BOOST: A supervised approach for multiple sclerosis lesion segmentation

作者：Cabezas Mariano^*; Oliver Arnau; Valverde Sergi; Beltran Brigitte; Freixenet Jordi; Vilanova Joan C; Ramio Torrenta Lluis; Rovira Alex; Llado Xavier

来源：Journal of Neuroscience Methods, 2014, 237: 108-117.

DOI：10.1016/j.jneumeth.2014.08.024

• Multiple sclerosis
• Brain analysis
• Image analysis
• Artificial intelligence
• Magnetic resonance imaging

Background: Automatic multiple sclerosis lesion segmentation is a challenging task. An extensive analysis of the most recent techniques indicates an improvement of the results obtained when using prior knowledge and contextual information. %26lt;br%26gt;New method: We present BOOST, a knowledge-based approach to automatically segment multiple sclerosis lesions through a voxel by voxel classification. We used the Gentleboost classifier and a set of features, including contextual features, registered atlas probability maps and an outlier map. %26lt;br%26gt;Results: Results are computed on a set of 45 cases from three different hospitals (15 of each), obtaining a moderate agreement between the manual annotations and the automatically segmented results. %26lt;br%26gt;Comparison with existing method(s): We quantitatively compared our results with three public state-of-the-art approaches obtaining competitive results and a better overlap with manual annotations. Our approach tends to better segment those cases with high lesion load, while cases with small lesion load are more difficult to accurately segment. %26lt;br%26gt;Conclusions: We believe BOOST has potential applicability in the clinical practice, although it should be improved in those cases with small lesion load.

• 出版日期2014-11-30