Automated identification of brain tumours from single MR images based on segmentation with refined patient-specific priors

• Main Authors: Ana eSanjuán, Cathy J Price, Laura eMancini, Goulven eJosse, Alice eGrogan, Adam K Yamamoto, Sharon eGeva, Alex P Leff, Tarek A Yousry, Mohamed L Seghier
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2013-12-01
• Series: Frontiers in Neuroscience
• Subjects: MRI; segmentation; spatial normalization; fuzzy clustering; Automated lesion identification
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00241/full

Brain tumours 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 tumour identification from single MR images. Our method rests on (A) a modified segmentation-normalisation procedure with an explicit extra prior for the tumour and (B) an outlier detection procedure for abnormal voxel (i.e. tumour) classification. To minimise tissue misclassification, the segmentation-normalisation procedure requires prior information of the tumour 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 tumours 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-behaviour mapping studies, or when lesion identification and/or spatial normalisation are problematic.