Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Multi-modal PixelNet for Brain Tumor Segmentation

Authors : Mobarakol Islam, Hongliang Ren

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

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Brain tumor segmentation using multi-modal MRI data sets is important for diagnosis, surgery and follow up evaluation. In this paper, a convolutional neural network (CNN) with hypercolumns features (e.g. PixelNet) utilizes for automatic brain tumor segmentation containing low and high-grade glioblastomas. Though pixel level convolutional predictors like CNNs, are computationally efficient, such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. PixelNet extracts features from multiple layers that correspond to the same pixel and samples a modest number of pixels across a small number of images for each SGD (Stochastic gradient descent) batch update. PixelNet has achieved whole tumor dice accuracy 87.6% and 85.8% for validation and testing data respectively in BraTS 2017 challenge.

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!

inform now

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!

inform now

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!

inform now
previous chapter Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
next chapter Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network
Literature
1.
Nyul, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)CrossRef
2.
Ellwaa, A., et al.: Brain tumor segmantation using random forest trained on iterative selected patients. In: Proceedings of BRATS-MICCAI (2016)
3.
Omuro, A., DeAngelis, L.M.: Glioblastoma and other malignant gliomas: a clinical review. JAMA 310, 1842–1850 (2013)CrossRef
4.
Bauer, S., et al.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58, R97 (2013)CrossRef
5.
Inda, M.M., Bonavia, R., Seoane, J.: Glioblastoma multiforme: a look inside its heterogeneous nature. Cancers 6(1), 226–239 (2014)CrossRef
6.
Baumgartner, C.F., Kamnitsas, K., Matthew, J., Smith, S., Kainz, B., Rueckert, D.: Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 203–211. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-46723-8_​24 CrossRef
7.
Azizi, S., Imani, F., Ghavidel, S., Tahmasebi, A., Kwak, J.T., Xu, S., Turkbey, B., Choyke, P., Pinto, P., Wood, B., Mousavi, P., Abolmaesumi, P.: Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study. Int. J. Comput. Assist. Radiol. Surg. 11(6), 947–956 (2016)CrossRef
8.
9.
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. arXiv:​1606.​04797v1 (2016)
10.
Havaei, M., Davy, A., Warde-Farley, D.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRef
11.
Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)CrossRef
12.
Kamnitsas, K., et al: DeepMedic on brain tumor segmentation. In: Proceedings of BRATS-MICCAI (2016)
13.
Pandian1, B., Boyle1, J., Orringer, D. A.: Multimodal tumor segmentation with 3D volumetric convolutional neural networks. In: Proceedings of BRATS-MICCAI (2016)
14.
Casamitjana, A., et al.: 3D convolutional networks for brain tumor segmentation. In: Proceedings of BRATS-MICCAI (2016)
15.
Alex, V., Krishnamurthi, G.: Brain tumor segmentation from multi modal MR images using stacked denoising autoencoders. In: Proceedings of BRATS-MICCAI (2016)
16.
Randhawa, R., Modi, A., Jain, P., Warier, P.: Improving segment boundary classification for brain tumor segmentation and longitudinal disease progression. In: Proceedings of BRATS-MICCAI (2016)
17.
Pereira, S., et al.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35, 1240–1251 (2016)CrossRef
18.
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, pp. 177–186. Springer, Heidelberg (2010)
19.
20.
Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: NIPS (2015)
21.
Hariharan, B., Arbelaez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: CVPR (2015)
22.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional models for semantic segmentation. In: CVPR (2015)
23.
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A. L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: ICLR (2015)
24.
25.
26.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556 (2014)
27.
Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34, 1993–2024 (2015)CrossRef
28.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby J. S., Freymann, J. B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data (2017, in Press)
29.
30.
31.
Zhao, X., Wu, Y., Song, G., Li, Z., Fan, Y., Zhang, Y.: Brain tumor segmentation using a fully convolutional neural network with conditional random fields. In: Proceedings of BRATS-MICCAI (2016)
32.
33.
Chen, H., Qi, X.J., Cheng, J.Z., Heng, P.A.: Deep contextual networks for neuronal structure segmentation. In: AAAI (2016)
34.
Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: deep contour-aware networks for accurate gland segmentation. In: CVPR, pp. 2487–2496 (2016)
35.
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. CoRR, vol. abs/1505.04597 (2015)
Metadata
Title
Multi-modal PixelNet for Brain Tumor Segmentation
Authors
Mobarakol Islam
Hongliang Ren
Copyright Year
2018
Book
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Print ISBN: 978-3-319-75237-2

Electronic ISBN: 978-3-319-75238-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75238-9_26

Premium Partner

    Image Credits