The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Automated segmentation of multiple sclerosis lesions by model outlier detection
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.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS)
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Multimodality image registration by maximization of mutual information
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563664 · Cited by 4,547
Self-citationBeitrag zur Theorie des Ferromagnetismus
Ising, E. (1925) · Zeitschrift für Physik · doi:10.1007/bf02980577 · Cited by 3,871
Cited by
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Self-citationBrosch, T., Tang, L. Y. W., Yoo, Y., Li, D. K. B., Traboulsee, A., & Tam, R. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528821 · Cited by 460
White matter lesion extension to automatic brain tissue segmentation on MRI
de Boer, R., Vrooman, H. A., van der Lijn, F., Vernooij, M. W., Ikram, M. A., van der Lugt, A., Breteler, M. M. B., & Niessen, W. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.011 · Cited by 329
Ghafoorian, M., Karssemeijer, N., Heskes, T., van Uden, I. W. M., Sanchez, C. I., Litjens, G., de Leeuw, F.-E., van Ginneken, B., Marchiori, E., & Platel, B. (2017) · Scientific Reports · doi:10.1038/s41598-017-05300-5 · Cited by 236
Demystification of AI-driven medical image interpretation: past, present and future
Savadjiev, P., Chong, J., Dohan, A., Vakalopoulou, M., Reinhold, C., Paragios, N., & Gallix, B. (2019) · European Radiology · doi:10.1007/s00330-018-5674-x · Cited by 161
Metadata
- Authors
- Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., & Suetens, P.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2001
- Volume
- 20
- Issue
- 8
- Pages
- 677-688
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2168399612
- Cited by
- 489
- References
- 5
- FWCI
- 15.67
- Type
- journal article
- Language
- en
- Open access
- closed
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- 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. https://doi.org/10.1109/42.938237
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
