Multimodal brain tumour segmentation using densely connected 3D convolutional neural network

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

Abstract

Reliable brain tumour segmentation methods from brain scans are essential for accurate diagnosis and treatment planning. In this paper, we propose a semantic segmentation method based on convolutional neural networks for brain tumour segmentation using multimodal brain scans. The proposed model is a modified version of the well-known U-net architecture. It gains from DenseNet blocks between the encoder and decoder parts of the U-net to transfer more semantic information from the input to the output. In addition, to speed up the training process, we employed deep supervision by adding segmentation blocks at the end of the decoder layers and summing up their outputs to generate the final output of the network. We trained and evaluated our model using the BraTS 2018 dataset. Comparing the results from the proposed model and a generic U-net, our model achieved higher segmentation accuracy in terms of the Dice score.

Original languageEnglish
Title of host publication2019 Digital Image Computing
Subtitle of host publicationTechniques and Applications (DICTA)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-5
Number of pages5
ISBN (Electronic)9781728138572, 9781728138565
ISBN (Print)9781728138589
DOIs
Publication statusPublished - 2019
Event2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019 - Perth, Australia
Duration: 2 Dec 20194 Dec 2019

Conference

Conference2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019
CountryAustralia
CityPerth
Period2/12/194/12/19

Keywords

  • Brain tumour segmentation
  • CNN
  • U-net
  • DenseNet
  • ResNet

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  • Cite this

    Ghaffari, M., Sowmya, A., Oliver, R., & Hamey, L. (2019). Multimodal brain tumour segmentation using densely connected 3D convolutional neural network. In 2019 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-5). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/DICTA47822.2019.8946023