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Automated segmentation of multiple sclerosis lesions by model outlier detection.
IEEE Trans Med Imaging. 2001 Aug; 20(8):677-88.IT

Abstract

This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expert segmentations, and between expert and automatic measurements.

Authors+Show Affiliations

Van Leemput K
Medical Image Computing, Faculties of Medicine and Engineering, University Hospital Gasthuisberg, Leuven, Belgium. koen.vanleemput@uz.kuleuven.ac.be
Maes F
No affiliation info available
Vandermeulen D
No affiliation info available
Colchester A
No affiliation info available
Suetens P
No affiliation info available

MeSH

AlgorithmsBrainHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMultiple Sclerosis

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

11513020

Citation

Van Leemput, K, et al. "Automated Segmentation of Multiple Sclerosis Lesions By Model Outlier Detection." IEEE Transactions On Medical Imaging, vol. 20, no. 8, 2001, pp. 677-88.
Van Leemput K, Maes F, Vandermeulen D, et al. Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging. 2001;20(8):677-88.
Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., & Suetens, P. (2001). Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Transactions On Medical Imaging, 20(8), 677-88.
Van Leemput K, et al. Automated Segmentation of Multiple Sclerosis Lesions By Model Outlier Detection. IEEE Trans Med Imaging. 2001;20(8):677-88. PubMed PMID: 11513020.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Automated segmentation of multiple sclerosis lesions by model outlier detection. AU - Van Leemput,K, AU - Maes,F, AU - Vandermeulen,D, AU - Colchester,A, AU - Suetens,P, PY - 2001/8/22/pubmed PY - 2002/1/16/medline PY - 2001/8/22/entrez SP - 677 EP - 88 JF - IEEE transactions on medical imaging JO - IEEE Trans Med Imaging VL - 20 IS - 8 N2 - This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expert segmentations, and between expert and automatic measurements. SN - 0278-0062 UR - https://www.unboundmedicine.com/medline/citation/11513020/Automated_segmentation_of_multiple_sclerosis_lesions_by_model_outlier_detection_ L2 - https://doi.org/10.1109/42.938237 DB - PRIME DP - Unbound Medicine ER -