An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation

Mark Lyksborg, Oula Puonti, Mikael Agn, Rasmus Larsen

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Accurate tumor segmentation plays an important role in radiosurgery planning and the assessment of radiotherapy treatment efficacy. In this paper we propose a method combining an ensemble of 2D convolutional neural networks for doing a volumetric segmentation of magnetic resonance images. The segmentation is done in three steps; first the full tumor region, is segmented from the background by a voxel-wise merging of the decisions of three networks learned from three orthogonal planes, next the segmentation is refined using a cellular automaton-based seed growing method known as growcut. Finally, within-tumor sub-regions are segmented using an additional ensemble of networks trained for the task. We demonstrate the method on the MICCAI Brain Tumor Segmentation Challenge dataset of 2014, and show improved segmentation accuracy compared to an axially trained 2D network and an ensemble segmentation without growcut. We further obtain competitive Dice scores compared with the most recent tumor segmentation challenge.
Original languageEnglish
Title of host publicationProceedings of the 19th Scandinavian Conference on Image Analysis, SCIA 2015
Publication date2015
ISBN (Print)978-3-319-19664-0
ISBN (Electronic)978-3-319-19665-7
Publication statusPublished - 2015
Event19th Scandinavian Conference on Image Analysis - Copenhagen, Denmark
Duration: 15 Jun 201517 Jun 2015
Conference number: 19


Conference19th Scandinavian Conference on Image Analysis
Internet address
SeriesLecture Notes in Computer Science


  • Tumor segmentation
  • Convolutional neural network
  • Ensemble classification
  • Cellular automaton


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