Abstract
Early stage brain tumor diagnosis can lead to proper treatment planning, which improves patient survival chances. A human expert advises an appropriate medical imaging scan based on the symptoms. Diagnosis done by a human expert is time-consuming, non-reproducible, and highly dependent on the expert’s expertise. The computerized analysis is preferred to help experts in diagnosis. The paper focuses on implementing a fully convolution neural network to segment brain tumor from MRI images. The proposed network achieves comparable dice similarity with reduced network parameters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agravat RR, Raval MS (2018) Deep learning for automated brain tumor segmentation in mri images. In: Soft computing based medical image analysis. Elsevier, pp 183–201
Agravat RR, Raval MS (2019) Brain tumor segmentation and survival prediction. In: International MICCAI brainlesion workshop. Springer, pp 338–348
Agravat RR, Raval MS (2019) Prediction of overall survival of brain tumor patients. In: TENCON 2019–2019 IEEE region 10 conference (TENCON). IEEE, pp 31–35
Agravat RR, Raval MS (2020) 3d semantic segmentation of brain tumor for overall survival prediction. In: International MICCAI brainlesion workshop. Springer, pp 215–227
Andermatt S, Pezold S, Cattin P (2016) Multi-dimensional gated recurrent units for the segmentation of biomedical 3d-data. In: Deep learning and data labeling for medical applications. Springer, pp 142–151
Andermatt S, Pezold S, Cattin P (2017) Multi-dimensional gated recurrent units for brain tumor segmentation. In: MICCAI multimodal brain tumor segmentation challenge (BraTS), pp 15–19
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495
Beers A, Chang K, Brown J, Sartor E, Mammen C, Gerstner E, Rosen B, Kalpathy Cramer J (2017) Sequential 3d u-nets for biologically-informed brain tumor segmentation. arXiv preprint arXiv:1709.02967
Casamitjana A, Puch S, Aduriz A, Sayrol E, Vilaplana V (2016) 3d convolutional networks for brain tumor segmentation. In: Proceedings of the MICCAI challenge on multimodal brain tumor image segmentation (BRATS), pp 65–68
Chen L, Wu Y, DSouza AM, Abidin AZ, Wismüller A, Xu C (2018) Mri tumor segmentation with densely connected 3d cnn. In: Medical imaging 2018: image processing, vol 10574. International Society for Optics and Photonics, p 105741F
Colmeiro RR, Verrastro C, Grosges T (2017) Multimodal brain tumor segmentation using 3d convolutional networks. In: International MICCAI brainlesion workshop. Springer, pp 226–240
Dong H, Yang G, Liu F, Mo Y, Guo Y (2017) Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In: Annual conference on medical image understanding and analysis. Springer, pp 506–517
Feng X, Meyer C (2017) Patch-based 3d u-net for brain tumor segmentation. In: 2017 international MICCAI BraTS challenge
Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31
Jain K (2015) Analytics Vidhya (2015), https://www.analyticsvidhya.com/blog/2017/08/10-advanced-deep-learningarchitectures-data-scientists/. Accessed 23 May 2018
Jia H, Bai C, Cai W, Huang H, Xia Y (2000) Hnf-netv2 for brain tumor segmentation using multi-modal mr imaging. arXiv preprint arXiv:2202.05268
Kamnitsas K, Ferrante E, Parisot S, Ledig C, Nori AV, Criminisi A, Rueckert lD, Glocker B, Deepmedic for brain tumor segmentation. In: International
Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med Image Anal 36:61–78
Kim G (2017) Brain tumor segmentation using deep fully convolutional neural networks. In: International MICCAI brainlesion workshop. Springer, pp 344–357
Li Y, Shen L (2017) Deep learning based multimodal brain tumor diagnosis. In: International MICCAI brainlesion workshop. Springer, pp 149–158
Li Z, Wang Y, Yu J (2017) Brain tumor segmentation using an adversarial network. In: International MICCAI brainlesion workshop. Springer, pp 123–132
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, et al (2015) The multimodal brain tumor image segmentation benchmark (brats). IEEE Transactions Med Imaging 34(10):1993–2024
Orhan AE, Pitkow X (2017) Skip connections eliminate singularities. arXiv preprint arXiv:1701.09175
Pereira S, Oliveira A, Alves V, Silva CA (2017) On hierarchical brain tumor segmentation in mri using fully convolutional neural networks: a preliminary study. In: Bioengineering (ENBENG), 2017 IEEE 5th Portuguese Meeting on, pp 1–4. IEEE
Rajput S, Agravat R, Roy M, Raval MS (2021) Glioblastoma multiforme patient survival prediction. In: International conference on medical imaging and computer-aided diagnosis. Springer, pp 47–58
Rezaei M, Harmuth K, Gierke W, Kellermeier T, Fischer M, Yang H, Meinel C (2017) Conditional adversarial network for semantic segmentation of brain tumor. arXiv preprint arXiv:1708.05227
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241
Saeed MU, Ali G, Bin W, Almotiri SH, AlGhamdi MA, Nagra AA, Masood K, Amin RU (2021) Rmu-net: a novel residual mobile u-net model for brain tumor segmentation from mr images. Electronics 10(16):1962
Shen H, Jianguo Z, Weishi Z (2017) Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation. In: 2017 IEEE International conference on image processing (ICIP). IEEE
Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4itk: improved n3 bias correction. IEEE Trans Med Imaging 29(6):1310–1320
Yan T, Ou Y, Huang T (2017) Automatic segmentation of brain tumor from mr images using segnet: selection of training data sets. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS) 309–312
Zhang T, Xu D, He K, Zhang H, Fu Y (2022) 3d u-net with trans-coder for brain tumor segmentation. In: Thirteenth international conference on graphics and image processing (ICGIP 2021), vol 12083, pp 540–548. SPIE
Acknowledgements
Authors would like to thank the support of NVIDIA Corporation with the donation of the Quadro K5200 and Quadro P5000 GPU used for this research, Dr. Krutarth Agravat (Medical Officer, Essar) for his help in discussion on tumors and analysis of the results. Authors are indebted to Mr. Pratik Chaudhary and Mr. Himanshu Budhia for their constant support during GPU installation and implementation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kapdi, R.A., Patel, J.A., Patel, J. (2023). Brain Tumor Segmentation Using Fully Convolution Neural Network. In: Singh, Y., Singh, P.K., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Proceedings of International Conference on Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 1001. Springer, Singapore. https://doi.org/10.1007/978-981-19-9876-8_1
Download citation
DOI: https://doi.org/10.1007/978-981-19-9876-8_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9875-1
Online ISBN: 978-981-19-9876-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)