Spatial information using CRF for brain tumor segmentation

Yawen Chen*, Sei Ichiro Kamata, Rong Fan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


In this work, we proposed a method combined the fuzzy spatial correlation of voxels in the MRI images obtained from a 3D network using CRF with the slice information captured by an ordinary 2D network to focus on the brain tumor segmentation task. Considering the expensive devices required by 3D networks while 2D networks can loss the information in the channel direction which leads to many false positive predictions, the proposed one can be a favorable direction to get more accurate features of the brain tumor. We take MRI images with 4 modalities in BRATS2018 dataset as the input of the 3D CNN after reducing the resolution. The CRF is used to calculate the neighboring correlation after the CNN feature extractor and can generate the probability map. The 2D network takes 2D slices in 4 modalities from the MRI images as input and output the segmentation map. The 2D segmentation maps are joining to 3D in order and combined with the probability map to get the final result. Compared with the state-of-the-art and the baseline method with the average Dice less than 0.85, the proposed is time and memory saving with the average Dice nearly 0.88.

Original languageEnglish
Title of host publicationThirteenth International Conference on Digital Image Processing, ICDIP 2021
EditorsXudong Jiang, Hiroshi Fujita
ISBN (Electronic)9781510646001
Publication statusPublished - 2021
Event13th International Conference on Digital Image Processing, ICDIP 2021 - Singapore, Singapore
Duration: 2021 May 202021 May 23

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference13th International Conference on Digital Image Processing, ICDIP 2021


  • Brain tumor
  • Conditional random field
  • Convolutional neural network
  • Deep learning
  • Segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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