Top

Published in:

2019 | OriginalPaper | Chapter

Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data

Authors : Hafsa Moontari Ali, M. Shamim Kaiser, Mufti Mahmud

Published in: Brain Informatics

Publisher: Springer International Publishing

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

search-config

AI-assisted search

Off

Abstract

Extracting knowledge from digital images largely depends on how well the mining algorithms can focus on specific regions of the image. In multimodality image analysis, especially in multi-layer diagnostic images, identification of regions of interest is pivotal and this is mostly done through image segmentation. Reliable medical image analysis for error-free diagnosis requires efficient and accurate image segmentation mechanisms. With the advent of advanced machine learning methods, such as deep learning (DL), in intelligent diagnostics, the requirement of efficient and accurate image segmentation becomes crucial. Targeting the beginners, this paper starts with an overview of Convolutional Neural Network, the most widely used DL technique and its application to segment brain regions from Magnetic Resonance Imaging. It then provides a quantitative analysis of the reviewed techniques as well as a rich discussion on their performance. Towards the end, few open challenges are identified and promising future works related to medical image segmentation using DL are indicated.

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 The Changes of Brain Networks Topology in Graph Theory of rt-fMRI Emotion Self-regulation Training

next chapter Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field

Bao, S., Chung, A.C.: Multi-scale structured CNN with label consistency for brain MRI segmentation. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 6(1), 113–117 (2018) CrossRef

Birenbaum, A., Greenspan, H.: Longitudinal multiple sclerosis lesion segmentation using multi-view convolutional neural networks. In: Carneiro, G., et al. (eds.) LABELS/DLMIA - 2016. LNCS, vol. 10008, pp. 58–67. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_7CrossRef

de Brebisson, A., Montana, G.: Deep neural networks for anatomical brain segmentation. In: Proceedings of IEEE CVPR, pp. 20–28 (2015)

Brosch, T., et al.: Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging 35(5), 1229–1239 (2016)CrossRef

Choi, H., Jin, K.H.: Fast and robust segmentation of the striatum using deep convolutional neural networks. J. Neurosci. Methods 274, 146–153 (2016)CrossRef

Dolz, J., et al.: HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation. IEEE Trans. Med. Imaging 38(5), 1116–1126 (2019)CrossRef

Dolz, J., Ayed, I.B., Yuan, J., Desrosiers, C.: Isointense infant brain segmentation with a hyper-dense connected CNN. In: Proceedings of ISBI 2018, pp. 616–620 (2018)

Dolz, J., Desrosiers, C., Ayed, I.B.: 3D fully convolutional networks for subcortical segmentation in MRI: a large-scale study. NeuroImage 170, 456–470 (2017)CrossRef

Ghafoorian, M., et al.: Non-uniform patch sampling with deep CNNs for white matter hyperintensity segmentation. In: Proceedings of ISBI, pp. 1414–1417 (2016)

10.

Ghafoorian, M., et al.: Location sensitive deep CNNs for segmentation of white matter hyperintensities. Sci. Rep. 7, 5110 (2017)CrossRef

11.

Guerrero, R., et al.: White matter hyperintensity and stroke lesion segmentation and differentiation using CNNs. NeuroImage: Clin. 17, 918–934 (2018)CrossRef

12.

Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRef

13.

He, Z., Bao, S., Chung, A.: 3D deep affine-invariant shape learning for brain MR image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS - 2018. LNCS, vol. 11045, pp. 56–64. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_7CrossRef

14.

Hoseini, F., Shahbahrami, A., Bayat, P.: An efficient implementation of deep convolutional neural networks for MRI segmentation. J. Digit. Imaging 31, 1–10 (2018)CrossRef

15.

Hussain, S., Anwar, S.M., Majid, M.: Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing 282, 248–261 (2018)CrossRef

16.

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

17.

Kumar, S., et al.: InfiNet: fully convolutional networks for infant brain MRI segmentation. In: Proceedings of ISBI 2018, pp. 145–148 (2018)

18.

Kushibar, K., et al.: Automated subcortical brain structure segmentation combining spatial and deep convolutional features. Med. Image Anal. 48, 177–186 (2018)CrossRef

19.

Laukamp, K.R., et al.: Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI. Eur. Radiol. 29(1), 124–132 (2018)CrossRef

20.

Le, T.H.N., Gummadi, R., Savvides, M.: Deep recurrent level set for segmenting brain tumors. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 646–653. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_74CrossRef

21.

Li, X., et al.: 3D multi-scale FCN with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality MRI. Med. Image Anal. 45, 41–54 (2018)CrossRef

22.

Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn Syst. 29(6), 2063–2079 (2018)MathSciNetCrossRef

23.

Miller, C.G., Krasnow, J., Schwartz, L.H.: Medical Imaging in Clinical Trials. Springer, London (2014). https://doi.org/10.1007/978-1-84882-710-3CrossRef

24.

Milletari, F., et al.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput. Vis. Image Underst. 164, 92–102 (2017)CrossRef

25.

Moeskops, P., et al.: Automatic segmentation of mr brain images with a CNN. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)CrossRef

26.

Moeskops, P., et al.: Deep learning for multi-task medical image segmentation in multiple modalities. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 478–486. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_55CrossRef

27.

Moeskops, P., Veta, M., Lafarge, M.W., Eppenhof, K.A.J., Pluim, J.P.W.: Adversarial training and dilated convolutions for brain MRI segmentation. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS - 2017. LNCS, vol. 10553, pp. 56–64. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_7CrossRef

28.

Moeskops, P., et al.: Evaluation of a deep learning approach for the segmentation of brain tissues and WMH of presumed vascular origin in MRI. NeuroImage: Clin. 17, 251–262 (2018)CrossRef

29.

Moeskops, P., Pluim, J.P.W.: Isointense infant brain MRI segmentation with a dilated convolutional neural network. CoRR abs/1708.02757 (2017)

30.

Nguyen, D.M., et al.: 3D-brain segmentation using deep neural network and Gaussian mixture model. In: Proceedings of WACV 2017, pp. 815–824 (2017)

31.

Nie, D., et al.: 3-D fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE Trans. Cybern. 49(3), 1123–1136 (2019)CrossRef

32.

Nie, D., Wang, L., Gao, Y., Sken, D.: FCNs for multi-modality isointense infant brain image segmentation. Proceedings of ISBI 2016, pp. 1342–1345 (2016)

33.

Pereira, S., et al.: Brain tumor segmentation using CNNs in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)CrossRef

34.

Rachmadi, M., et al.: Segmentation of WMHs using CNNs with global spatial information in routine clinical brain MRI with none or mild vascular pathology. Comput. Med. Imaging Graph. 66, 28–43 (2018)CrossRef

35.

Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRef

36.

Roy, S., et al.: Multiple sclerosis lesion segmentation from brain MRI via fully convolutional neural networks. arXiv preprint arXiv:1803.09172 (2018)

37.

Shakeri, M., et al.: Sub-cortical brain structure segmentation using F-CNN’s. In: Proceedings of ISBI 2016, pp. 269–272. IEEE (2016)

38.

Shen, G., et al.: Brain tumor segmentation using concurrent fully convolutional networks and conditional random fields. In: Proceedings of ICMIP 2018, pp. 24–30 (2018)

39.

Valverde, S., et al.: Improving automated multiple sclerosis lesion segmentation with a cascaded 3D CNN approach. NeuroImage 155, 159–168 (2017)CrossRef

40.

Wachinger, C., Reuter, M., Klein, T.: DeepNAT: deep CNN for segmenting neuroanatomy. NeuroImage 170, 434–445 (2018)CrossRef

41.

Wang, Y., et al.: Automatic tumor segmentation with deep convolutional neural networks for radiotherapy applications. Neural Process. Lett. 48, 1–12 (2018)CrossRef

42.

Xu, B., et al.: Orchestral fully convolutional networks for small lesion segmentation in brain MRI. In: Proceedings of ISBI 2018, pp. 889–892. IEEE (2018)

43.

Yi, D., Zhou, M., Chen, Z., Gevaert, O.: 3-D convolutional neural networks for glioblastoma segmentation. CoRR abs/1611.04534 (2016)

44.

Zeng, G., Zheng, G.: Multi-stream 3D FCN with MSDS for multi-modality isointense infant brain MRI segmentation. In: Proceedings of ISBI 2018, pp. 136–140 (2018)

45.

Zhang, W., et al.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015)CrossRef

46.

Zhao, X., et al.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98–111 (2018)CrossRef

47.

Zhou, C., Ding, C., Lu, Z., Wang, X., Tao, D.: One-pass multi-task convolutional neural networks for efficient brain tumor segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 637–645. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_73CrossRef

48.

Zhuge, Y., et al.: Brain tumor segmentation using holistically-nested neural networks in MRI images. Med. Phys. 44(10), 5234–5243 (2017)CrossRef

Title: Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data
Authors: Hafsa Moontari Ali
M. Shamim Kaiser
Mufti Mahmud
Publisher: Springer International Publishing
Book: Brain Informatics
Print ISBN: 978-3-030-37077-0

Electronic ISBN: 978-3-030-37078-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-37078-7_14

Springer Professional

Abstract

Please log in to get access to your license.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner