Abstract
We propose a new approach to Multiple Sclerosis lesion segmentation that utilizes synthesized images. A new method of image synthesis is considered: joint intensity fusion (JIF). JIF synthesizes an image from a library of deformably registered and intensity normalized atlases. Each location in the synthesized image is a weighted average of the registered atlases; atlas weights vary spatially. The weights are determined using the joint label fusion (JLF) framework. The primary methodological contribution is the application of JLF to MRI signal directly rather than labels. Synthesized images are then used as additional features in a lesion segmentation task using the OASIS classifier, a logistic regression model on intensities from multiple modalities. The addition of JIF synthesized images improved the Dice-Sorensen coefficient (relative to manually drawn gold standards) of lesion segmentations over the standard model segmentations by \(0.0462 \pm 0.0050\) (mean ± standard deviation) at optimal threshold over all subjects and 10 separate training/testing folds.
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Acknowledgements
This work supported by the National Institutes of Health grants: R01-NS094456, R01-EB017255, R01-NS085211, R21-NS093349, R01-NS082347, R01-NS070906 and by the National Multiple Sclerosis Society grant: RG-1507-05243.
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Fleishman, G.M. et al. (2018). Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_4
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