This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation in MR images. We address the problem using three different architectures that combine fine and coarse features to obtain the final segmentation. We compare three different networks that use multi-resolution features in terms of both design and performance and we show that they improve their single-resolution counterparts.
CITATION STYLE
Casamitjana, A., Puch, S., Aduriz, A., & Vilaplana, V. (2016). 3D convolutional neural networks for brain tumor segmentation: A comparison of multi-resolution architectures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10154 LNCS, pp. 150–161). Springer Verlag. https://doi.org/10.1007/978-3-319-55524-9_15
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