Abstract
Quantification of white matter lesion changes on brain magnetic resonance (MR) images is of major importance for the follow-up of patients with Multiple Sclerosis (MS). Many automated segmentation methods have been proposed. However, most of them focus on a single time point MR scan session and hence lack consistency when evaluating lesion changes over time. In this paper, we present MSmetrix-long, an unsupervised method that incorporates temporal consistency by jointly segmenting MS lesions of two subsequent scan sessions. The method is formulated as a Maximum A Posteriori model on the FLAIR image intensities of both time points and the difference image intensities, and optimised using an expectation maximisation algorithm. Validation is performed on two different data sets in terms of consistency and sensitivity to MS lesion changes. It is shown that MSmetrix-long outperforms MSmetrix-cross for the quantification of MS lesion evolution over time.
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Jain, S., Ribbens, A., Sima, D.M., Van Huffel, S., Maes, F., Smeets, D. (2017). Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation. In: Müller, H., et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_19
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DOI: https://doi.org/10.1007/978-3-319-61188-4_19
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