@article {802,
	title = {OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.},
	journal = {Neuroimage Clin},
	volume = {2},
	year = {2013},
	month = {2013},
	pages = {402-13},
	abstract = {<p>Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1\% and below of 0.59\% (95\% CI; [0.50\%, 0.67\%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74\% (95\% CI: [65\%, 82\%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77\% (95\% CI: [71\%, 83\%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76\% (95\% CI: [64\%, 88\%]) of cases, the neurologist 66\% (95\% CI: [52\%, 78\%]) and the radiologist 52\% (95\% CI: [38\%, 66\%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.</p>},
	issn = {2213-1582},
	doi = {10.1016/j.nicl.2013.03.002},
	author = {Sweeney, Elizabeth M and Shinohara, Russell T and Shiee, Navid and Mateen, Farrah J and Chudgar, Avni A and Cuzzocreo, Jennifer L and Calabresi, Peter A and Pham, Dzung L and Reich, Daniel S and Crainiceanu, Ciprian M}
}
@article {pmid22006982,
	title = {Optical coherence tomography segmentation reveals ganglion cell layer pathology after optic neuritis},
	journal = {Brain},
	volume = {135},
	number = {Pt 2},
	year = {2012},
	month = {Feb},
	pages = {521{\textendash}533},
	author = {Syc, S. B. and Saidha, S. and Newsome, S and Ratchford, J and Levy, M. and Ford, E. and Crainiceanu, C and Durbin, M. K. and Oakley, J. D. and Meyer, S. A. and Frohman, E. M. and Calabresi, P}
}
@article {caffo2009overview,
	title = {An overview of observational sleep research with application to sleep stage transitioning},
	journal = {Chance, PMCID: PMC2800358},
	volume = {22},
	number = {1},
	year = {2009},
	pages = {10{\textendash}15},
	publisher = {Springer},
	author = {Caffo, B and Swihart, B and Crainiceanu, C and Laffan, A and Punjabi, N}
}
@article {Caff:Swih:Crai:Laff:Punj:an:2009,
	title = {An Overview of Observational Sleep Research with Application to Sleep State Transitioning},
	journal = {Chance},
	volume = {22},
	number = {1},
	year = {2009},
	pages = {10{\textendash}15},
	author = {Caffo, B and Swihart, B and Crainiceanu, C and Laffan, A and Punjabi, Naresh}
}
@article {choi2007optimal,
	title = {Optimal sampling times in bioequivalence studies using a simulated annealing algorithm},
	journal = {Statistics and Computing},
	volume = {17},
	number = {4},
	year = {2007},
	pages = {337{\textendash}347},
	publisher = {Springer},
	author = {Choi, L and Caffo, B and Rohde, C}
}
