Abstract
Brain tumour identification with traditional magnetic resonance imaging (MRI) tends to be time-consuming and in most cases, reading of the resulting images by human agents is prone to error, making it desirable to use automated image segmentation. This is a multi-step process involving: (a) collecting data in the form of raw processed or raw images, (b) removing bias by using pre-processing, (c) processing the image and locating the brain tumour, and (d) showing the tumour affected areas on a computer screen or projector. Several systems have been proposed for medical image segmentation but have not been evaluated in the field. This may be due to ongoing issues of image clarity, grey and white matter present in a scan image, lack of knowledge of the end user and constraints arising from MRI imaging systems. This makes it imperative to develop a comprehensive technique for the accurate diagnosis of brain tumors in MRI images. In this paper, we introduce a taxonomy consisting of ‘Data, Image segmentation processing, and View’ (DIV) which are the major components required to develop a high-end system for brain tumour diagnosis based on deep learning neural networks. The DIV taxonomy is evaluated based on system completeness and acceptance. The utility of the DIV taxonomy is demonstrated by classifying 30 state-of-the-art publications in the domain of medFical image segmentation systems based on deep neural networks. The results demonstrate that few components of medical image segmentation systems have been validated although several have been evaluated by identifying role and efficiency of the components in this domain.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- MRI:
-
Magnetic resonance imaging
- MCFM:
-
Modified fuzzy C-means
- CLE:
-
Confocal laser endomicroscopy
- CNN:
-
Convolutional neural networks
- DCNN:
-
Deep conventional neural network
- ACM:
-
Active contour models
- CRFs:
-
Conditional random fields
- FCNN:
-
Fully convolutional neural network
- LHNPSO:
-
Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight
- KFECSB:
-
Kernelized fuzzy entropy clustering with spatial information and bias correction
- RF Classifier:
-
Random forests classifier
References
Ali A, Yangyu F (2017) Unsupervised feature learning and automatic modulation classification using deep learning model. Phys Commun 25:75–84. https://doi.org/10.1016/j.phycom.2017.09.004
Al-Milaji Z, Ersoy I, Hafiane A, Palaniappan K, Bunyak F (2017) Integrating segmentation with deep learning for enhanced classification of epithelial and stromal tissues in H & E images. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2017.09.015
Amin J, Sharif M, Yasmin M, Fernandes SL (2018) Big data analysis for brain tumor detection: deep convolutional neural networks. Future Gener Comput Syst 87:290–297. https://doi.org/10.1016/j.future.2018.04.065
Banerjee S, Mitra S, Uma Shankar B (2018) Automated 3D segmentation of brain tumor using visual saliency. Inf Sci 424:337–353. https://doi.org/10.1016/j.ins.2017.10.011
Bonte S, Goethals I, Van Holen R (2018) Machine learning based brain tumor segmentation on limited data using local texture and abnormality. Comput Biol Med 98:39–47. https://doi.org/10.1016/j.compbiomed.2018.05.005
Cabria I, Gondra I (2017) MRI segmentation fusion for brain tumor detection. Inf Fusion 36:1–9. https://doi.org/10.1016/j.inffus.2016.10.003
Charron O, Lallement A, Jarnet D, Noblet V, Clavier J-B, Meyer P (2018) Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med 95:43–54. https://doi.org/10.1016/j.compbiomed.2018.02.004
Chen L, Bentley P, Rueckert D (2017a) Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage Clin 15:633–643. https://doi.org/10.1016/j.nicl.2017.06.016
Chen R-M, Yang S-C, Wang C-M (2017b) MRI brain tissue classification using unsupervised optimized extenics-based methods. Comput Electr Eng 58:489–501. https://doi.org/10.1016/j.compeleceng.2017.01.018
Chen H, Dou Q, Yu L, Qin J, Heng P-A (2018) VoxResNet: deep voxelwise residual networks for brain segmentation from 3DMR images. NeuroImage 170:446–455. https://doi.org/10.1016/j.neuroimage.2017.04.041
Cole BL, Pritchard CC, Anderson M, Leary SE (2018) Targeted sequencing of malignant supratentorial pediatric brain tumors demonstrates a high frequency of clinically relevant mutations. Pediatr Dev Pathol 21(4):380–388. https://doi.org/10.1177/1093526617743905
Dou Q, Yu L, Chen H, Jin Y, Yang X, Qin J, Heng P-A (2017) 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal 41:40–54. https://doi.org/10.1016/J.MEDIA.2017.05.001
Essadike A, Ouabida E, Bouzid A (2018) Brain tumor segmentation with Vander Lugtcorrelator based active contour. Comput Methods Progr Biomed 160:103–117. https://doi.org/10.1016/j.cmpb.2018.04.004
Fageot J, Al-Kadi OS (2017) Fundamentals of texture processing for biomedical image analysis: a general definition and problem formulation. Biomed Texture Anal. https://doi.org/10.1016/b978-0-12-812133-7.00001-6
Farhi L, Yusuf A, Raza RH (2017) Adaptive stochastic segmentation via energy-convergence for brain tumor in MR images. J Vis Commun Image Represent 46:303–311. https://doi.org/10.1016/j.jvcir.2017.04.013
Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G et al (2018) NiftyNet: a deep-learning platform for medical imaging. Comput Methods Progr Biomed 158:113–122. https://doi.org/10.1016/j.cmpb.2018.01.025
Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y et al (2017) Brain tumor segmentation with Deep Neural Networks. Med Image Anal 35:18–31. https://doi.org/10.1016/j.media.2016.05.004
Hussain S, Anwar SM, Majid M (2017) Segmentation of glioma tumors in brain using deep convolutional neural network. https://doi.org/10.1016/j.neucom.2017.12.032
Ibrahim RW, Hasan AM, Jalab HA (2018) A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans. Comput Methods Progr Biomed 163:21–28. https://doi.org/10.1016/j.cmpb.2018.05.031
Ilunga-Mbuyamba E, Avina-Cervantes JG, Cepeda-Negrete J, Ibarra-Manzano MA, Chalopin C (2017a) Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation. Comput Biol Med 91:69–79. https://doi.org/10.1016/j.compbiomed.2017.10.003
Ilunga-Mbuyamba E, Avina-Cervantes JG, Garcia-Perez A, de Romero-Troncoso R, Aguirre-Ramos H, Cruz-AcevesChalopin IC (2017b) Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation. Neurocomputing 220:84–97. https://doi.org/10.1016/j.neucom.2016.07.057
Izadyyazdanabadi M, Belykh E, Mooney M, Martirosyan N, Eschbacher J, Nakaji P et al (2018) Convolutional neural networks: ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical CLE images. J Vis Commun Image Represent 54:10–20. https://doi.org/10.1016/j.jvcir.2018.04.004
Karthikumar SK, Chitra P (2019) Implementing cooperative bacterial foraging optimisation algorithm-based resources and VM management in IaaS cloud. Int J Knowl Manag Stud 10(1):69–83
Kawahara J, Brown CJ, Miller SP, Booth BG, Chau V, Grunau RE et al (2017) BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146:1038–1049. https://doi.org/10.1016/j.neuroimage.2016.09.046
Kaya IE, Pehlivanlı AÇ, Sekizkardeş EG, Ibrikci T (2017) PCA based clustering for brain tumor segmentation of T1w MRI images. Comput Methods Progr Biomed 140:19–28. https://doi.org/10.1016/j.cmpb.2016.11.011
Koley S, Sadhu AK, Mitra P, Chakraborty B, Chakraborty C (2016) Delineation and diagnosis of brain tumors from post contrast T1-weighted MR images using rough granular computing and random forest. Appl Soft Comput 41:453–465. https://doi.org/10.1016/j.asoc.2016.01.022
Li Y, Jia F, Qin J (2016) Brain tumor segmentation from multimodal magnetic resonance images via sparse representation. Artif Intell Med 73:1–13. https://doi.org/10.1016/j.artmed.2016.08.004
Lim KY, Mandava R (2018) A multi-phase semi-automatic approach for multisequence brain tumor image segmentation. Expert Syst Appl 112:288–300. https://doi.org/10.1016/j.eswa.2018.06.041
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005
Mohan G, Subashini M (2019) Chapter 4 - Medical imaging with intelligent systems: a review. In: Deep learning and parallel computing environment for bioengineering systems, pp 53–73. https://doi.org/10.1016/B978-0-12-816718-2.00011-7
Na S, Li L, Crosson B, Dotson V, MacDonald TJ, Mao H, King TZ (2018) White matter network topology relates to cognitive flexibility and cumulative neurological risk in adult survivors of pediatric brain tumors. NeuroImage Clin 20:485–497. https://doi.org/10.1016/j.nicl.2018.08.015
Nabizadeh N, Kubat M (2017) Automatic tumor segmentation in single-spectral MRI using a texture-based and contour-based algorithm. Expert Syst Appl 77:1–10. https://doi.org/10.1016/j.eswa.2017.01.036
Naito T, Nagashima Y, Taira K, Uchio N, Tsuji S, Shimizu J (2017) Identification and segmentation of myelinated nerve fibers in a cross-sectional optical microscopic image using a deep learning model. J Neurosci Methods 291:141–149. https://doi.org/10.1016/J.JNEUMETH.2017.08.014
Namburu A, Samay S, Edara SR (2017) Soft fuzzy rough set-based MR brain image segmentation. Appl Soft Comput 54:456–466. https://doi.org/10.1016/j.asoc.2016.08.020
Nithila EE, Kumar SS (2017) Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images. Eng Sci Technol Int J 20(3):1192–1202. https://doi.org/10.1016/j.jestch.2016.12.006
Pak RW, Hadjiabadi DH, Senarathna J, Agarwal S, Thakor NV, Pillai JJ, Pathak AP (2017) Implications of neurovascular uncoupling in functional magnetic resonance imaging (fMRI) of brain tumors. J Cereb Blood Flow Metab 37(11):3475–3487. https://doi.org/10.1177/0271678x17707398
Pan X, Li L, Yang H, Liu Z, Yang J, Zhao L, Fan Y (2017) Accurate segmentation of nuclei in pathological images via sparse reconstruction and deep convolutional networks. Neurocomputing 229:88–99. https://doi.org/10.1016/J.NEUCOM.2016.08.103
Pham TX, Siarry P, Oulhadj H (2018) Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation. Appl Comput 65:230–242. https://doi.org/10.1016/j.asoc.2018.01.003
Pinto A, Pereira S, Rasteiro D, Silva CA (2018) Hierarchical brain tumor segmentation using extremely randomized trees. Pattern Recogn 82:105–117. https://doi.org/10.1016/j.patcog.2018.05.006
Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S (2017) Entropy based segmentation of tumor from brain MR images—a study with teaching learning-based optimization. Pattern Recogn Lett 94:87–95. https://doi.org/10.1016/j.patrec.2017.05.028
Raju AR, Suresh P, Rao RR (2018) Bayesian HCS-based multi-SVNN: a classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybern Biomed Eng 38(3):646–660. https://doi.org/10.1016/j.bbe.2018.05.001
Saha S, Alok AK, Ekbal A (2016) Brain image segmentation using semi-supervised clustering. Expert Syst Appl 52:50–63. https://doi.org/10.1016/j.eswa.2016.01.005
Singh C, Bala A (2018) A DCT-based local and non-local fuzzy C-means algorithm for segmentation of brain magnetic resonance images. Appl Soft Comput 68:447–457. https://doi.org/10.1016/j.asoc.2018.03.054
Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR et al (2018) Supervised learning based multimodal MRI brain tumor segmentation using texture features from supervoxels. Comput Methods Progr Biomed 157:69–84. https://doi.org/10.1016/j.cmpb.2018.01.003
Sompong C, Wongthanavasu S (2017) An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm. Expert Syst Appl 72:231–244. https://doi.org/10.1016/j.eswa.2016.10.064
Subudhi BN, Thangaraj V, Sankaralingam E, Ghosh A (2016) Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation. Magn Reson Imaging 34(9):1292–1304. https://doi.org/10.1016/j.mri.2016.07.002
Valable S, Corroyer-Dulmont A, Chakhoyan A, Durand L, Toutain J, Divoux D et al (2017) Imaging of brain oxygenation with magnetic resonance imaging: a validation with positron emission tomography in the healthy and tumoral brain. J Cereb Blood Flow Metab 37(7):2584–2597. https://doi.org/10.1177/0271678x16671965
Valverde S, Cabezas M, Roura E, González-Villà S, Pareto D, Vilanova JC et al (2017) Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage 155:159–168. https://doi.org/10.1016/j.neuroimage.2017.04.034
Vishnuvarthanan G, Rajasekaran MP, Subbaraj P, Vishnuvarthanan A (2016) An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput 38:190–212. https://doi.org/10.1016/j.asoc.2015.09.016
Vishnuvarthanan A, Rajasekaran MP, Govindaraj V, Zhang Y, Thiyagarajan A (2017) An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images. Appl Soft Comput 57:399–426. https://doi.org/10.1016/j.asoc.2017.04.023
Vishnuvarthanan A, Rajasekaran MP, Govindaraj V, Zhang Y, Thiyagarajan A (2018) Development of a combinational framework to concurrently perform tissue segmentation and tumor identification in T1–W, T2–W, FLAIR and MPR type magnetic resonance brain images. Expert Syst Appl 95:280–311. https://doi.org/10.1016/j.eswa.2017.11.040
Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98–111. https://doi.org/10.1016/J.MEDIA.2017.10.002
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Devunooru, S., Alsadoon, A., Chandana, P.W.C. et al. Deep learning neural networks for medical image segmentation of brain tumours for diagnosis: a recent review and taxonomy. J Ambient Intell Human Comput 12, 455–483 (2021). https://doi.org/10.1007/s12652-020-01998-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-01998-w