Automatic segmentation and classification of multiple sclerosis in multichannel MRI.
We introduce a multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in automatically detecting multiple sclerosis (MS) lesions in 3-D multichannel magnetic resonance (MR) images. Our method uses segmentation to obtain a hierarchical decomposition of a multichannel, anisotropic MR scans. It then produces a rich set of features describing the segments in terms of intensity, shape, location, neighborhood relations, and anatomical context. These features are then fed into a decision forest classifier, trained with data labeled by experts, enabling the detection of lesions at all scales. Unlike common approaches that use voxel-by-voxel analysis, our system can utilize regional properties that are often important for characterizing abnormal brain structures. We provide experiments on two types of real MR images: a multichannel proton-density-, T2-, and T1-weighted dataset of 25 MS patients and a single-channel fluid attenuated inversion recovery (FLAIR) dataset of 16 MS patients. Comparing our results with lesion delineation by a human expert and with previously extensively validated results shows the promise of the approach.
Akselrod-Ballin
Ayelet
A
Computational Radiology Laboratory, Children's Hospital, Harvard Medical School, Boston, MA 02115, USA. Ayelet.Akselrod-Ballin@childrens.harvard.edu
Galun
Meirav
M
Gomori
John Moshe
JM
Filippi
Massimo
M
Valsasina
Paola
P
Basri
Ronen
R
Brandt
Achi
A
D000328:Adult; D000465:Algorithms; D016880:Anisotropy; D001921:Brain; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000175:diagnosis*; D015203:Reproducibility of Results