Skip to main content
Top

2021 | OriginalPaper | Chapter

TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI Data

Authors : Keerati Kaewrak, John Soraghan, Gaetano Di Caterina, Derek Grose

Published in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

A novel encoder-decoder deep learning network called TwoPath U-Net for multi-class automatic brain tumor segmentation task is presented. The network uses cascaded local and global feature extraction paths in the down-sampling path of the network which allows the network to learn different aspects of both the low-level feature and high-level features. The proposed network architecture using a full image and patches input technique was used on the BraTS2020 training dataset. We tested the network performance using the BraTS2019 validation dataset and obtained the mean dice score of 0.76, 0.64, and 0.58 and the Hausdorff distance 95% of 25.05, 32.83, and 37.57 for the whole tumor, tumor core and enhancing tumor regions.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
2.
go back to reference Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv Prepr. arXiv:1811.02629 (2018) Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv Prepr. arXiv:​1811.​02629 (2018)
6.
go back to reference Havaei, M., Dutil, F., Pal, C., Larochelle, H., Jodoin, P.-M.: A convolutional neural network approach to brain tumor segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. LNCS, vol. 9556, pp. 195–208. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30858-6_17 Havaei, M., Dutil, F., Pal, C., Larochelle, H., Jodoin, P.-M.: A convolutional neural network approach to brain tumor segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. LNCS, vol. 9556, pp. 195–208. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-30858-6_​17
9.
go back to reference Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In: Valdés Hernández, M., González-Castro, V. (eds.) Medical Image Understanding and Analysis. MIUA 2017. CCIS, vol. 723, pp. 506–517. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_44 Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In: Valdés Hernández, M., González-Castro, V. (eds.) Medical Image Understanding and Analysis. MIUA 2017. CCIS, vol. 723, pp. 506–517. Springer, Cham (2017). https://​doi.​org/​10.​1007/​978-3-319-60964-5_​44
10.
go back to reference Kaewrak, K., Soraghan, J., Caterina, G.D., Grose, D.: Modified U-Net for automatic brain tumor regions segmentation. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1–5 (2019) Kaewrak, K., Soraghan, J., Caterina, G.D., Grose, D.: Modified U-Net for automatic brain tumor regions segmentation. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1–5 (2019)
11.
go back to reference Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded u-net: 1st place solution to BraTS challenge 2019 segmentation task. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. LNCS, vol. 11992, pp. 231–241. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46640-4_22 Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded u-net: 1st place solution to BraTS challenge 2019 segmentation task. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. LNCS, vol. 11992, pp. 231–241. Springer, Cham (2020). https://​doi.​org/​10.​1007/​978-3-030-46640-4_​22
13.
go back to reference Bakas, S., Akbari, H., Sotiras, A.: Segmentation labels for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017) Bakas, S., Akbari, H., Sotiras, A.: Segmentation labels for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017)
14.
go back to reference Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. 286 (2017) Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. 286 (2017)
17.
go back to reference Sim, K.S., Nia, M.E., Tso, C.P., Kho, T.K.: Brain Ventricle Detection Using Hausdorff Distance. In: Tran, Q.N., Arabnia Bioinformatics, and Systems Biology, H.R.B.T.-E.T. in A. and I. for C.B. (eds.) Emerging Trends in Computer Science and Applied Computing. pp. 523–531. Morgan Kaufmann, Boston (2016) Sim, K.S., Nia, M.E., Tso, C.P., Kho, T.K.: Brain Ventricle Detection Using Hausdorff Distance. In: Tran, Q.N., Arabnia Bioinformatics, and Systems Biology, H.R.B.T.-E.T. in A. and I. for C.B. (eds.) Emerging Trends in Computer Science and Applied Computing. pp. 523–531. Morgan Kaufmann, Boston (2016)
Metadata
Title
TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI Data
Authors
Keerati Kaewrak
John Soraghan
Gaetano Di Caterina
Derek Grose
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-72087-2_26

Premium Partner