Mariano Cabezas1, Arnau Oliver2, Sergi Valverde2, Brigitte Beltran3, Jordi Freixenet2, Joan C Vilanova4, Lluís Ramió-Torrentà3, Alex Rovira5, Xavier Lladó2. 1. Department of Computer Architecture and Technology, University of Girona, Spain; Magnetic Resonance Unit, Department of Radiology, Vall d'Hebron University Hospital, Spain. Electronic address: mcabezas@eia.udg.edu. 2. Department of Computer Architecture and Technology, University of Girona, Spain. 3. Multiple Sclerosis and Neuroimmunology Unit, Dr. Josep Trueta University Hospital, Spain. 4. Girona Magnetic Resonance Center, Spain. 5. Magnetic Resonance Unit, Department of Radiology, Vall d'Hebron University Hospital, Spain.
Abstract
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. 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. 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. 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. CONCLUSIONS: We believe BOOST has potential applicability in the clinical practice, although it should be improved in those cases with small lesion load.
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. 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. 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. 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. CONCLUSIONS: We believe BOOST has potential applicability in the clinical practice, although it should be improved in those cases with small lesion load.
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173