Convolutional Neural Network Based Segmentation of Demyelinating Plaques in MRI
In this paper a new architecture of convolutional neural networks is proposed. It is a fully-convolutional architecture which allows to keep the size of the processed image constant. This, in consequence, allows to apply it for image segmentation tasks where for a given image a mask representing sought regions should be produced. An additional advantage of this architecture is its ability to learn from smaller images which reduces the amount of data that must be propagated through the network. The trained network can be still applied to images of any size. The proposed method was used for automatic localization of demyelinating plaques in head MRI sequences. This work was possible, which should be emphasized, only thanks to the manually outlined plaques provided by radiologist. To present characteristic of the considered approach three architectures and three result evaluation methods were discussed and compared.
KeywordsMultiple sclerosis Segmentation Machine learning Convolutional neural networks
This project has been partly funded with support from National Science Centre, Republic of Poland, decision number DEC-2012/05/D/ST6/03091.
Authors would like to express their gratitude to the Department of Radiology of Barlicki University Hospital in Lodz for making head MRI sequences available.
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