title: Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors.
abstract: Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit "extra prior" for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic.
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College of London London, UK ; Departamento de Psicología Básica, Clínica y Psicobiología, Universitat Jaume I Castellón, Spain.
authors.forename: Ana
authors.initials: A
authors.lastname: Sanjuán
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College of London London, UK.
authors.forename: Cathy J
authors.initials: CJ
authors.lastname: Price
authors.affiliation: Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery London, UK.
authors.forename: Laura
authors.initials: L
authors.lastname: Mancini
authors.affiliation: Hôpital de la Pitié-Salpêtrière, Institut du Cerveau et de la Moëlle épinière Paris, France.
authors.forename: Goulven
authors.initials: G
authors.lastname: Josse
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College of London London, UK.
authors.forename: Alice
authors.initials: A
authors.lastname: Grogan
authors.affiliation: Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery London, UK.
authors.forename: Adam K
authors.initials: AK
authors.lastname: Yamamoto
authors.affiliation: Developmental Cognitive Neuroscience Unit, Institute of Child Health, University College of London London, UK.
authors.forename: Sharon
authors.initials: S
authors.lastname: Geva
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College of London London, UK ; Institute of Cognitive Neuroscience, University College of London London, UK.
authors.forename: Alex P
authors.initials: AP
authors.lastname: Leff
authors.affiliation: Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery London, UK.
authors.forename: Tarek A
authors.initials: TA
authors.lastname: Yousry
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College of London London, UK.
authors.forename: Mohamed L
authors.initials: ML
authors.lastname: Seghier
keywords: MRI; automatic lesion identification; fuzzy clustering; segmentation; spatial normalization