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3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10154)

Abstract

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.

Keywords

  • Receptive Field
  • Convolutional Neural Network
  • Final Segmentation
  • Memory Constraint
  • Convolutional Layer

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This work has been partially supported by the project BIGGRAPH-TEC2013-43935-R, financed by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF). Adrià Casamitjana is supported by the Spanish “Ministerio de Educación, Cultura y Deporte” FPU Research Fellowship.

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Correspondence to Adrià Casamitjana .

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Casamitjana, A., Puch, S., Aduriz, A., Vilaplana, V. (2016). 3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-55524-9_15

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