Abstract
We analyze the performance of multi-dimensional gated recurrent units on automated lesion segmentation in multiple sclerosis. The segmentation of these pathologic structures is not trivial, since location, shape and size can be arbitrary. Furthermore, the inherent class imbalance of about 1 lesion voxel to 10 000 healthy voxels further exacerbates the correct segmentation. We introduce a new MD-GRU setup, using established techniques from the deep learning community as well as our own adaptations. We evaluate these modifications by comparing them to a standard MD-GRU network. We demonstrate that using data augmentation, selective sampling, residual learning and/or DropConnect on the RNN state can produce better segmentation results. Reaching rank #1 in the ISBI 2015 longitudinal multiple sclerosis lesion segmentation challenge, we show that a setup which combines these techniques can outperform the state of the art in automated lesion segmentation.
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Andermatt, S., Pezold, S., Cattin, P.C. (2018). Automated Segmentation of Multiple Sclerosis Lesions Using Multi-dimensional Gated Recurrent Units. 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_3
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DOI: https://doi.org/10.1007/978-3-319-75238-9_3
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