Abstract
Locating brain tumor and its various sub-regions are crucial for treating tumor in humans. The challenge lies in taking cues for identification of tumors having different size, shape, and location in the brain using multimodal data. Numerous work has been done in the recent past in BRATS challenge [16]. In this work, an ensemble based approach using Deep Random Forest [23] in incremental learning mechanism is deployed. The proposed approach divides data and features into disjoint subsets and learn in chunk as cascading architecture of multi layer RFs. Each layer is also a combination of RFs to use sample of the data to learn diversity present. Given the huge amount of data, the proposed approach is fast and paralleled. In addition, we have proposed new kind of Local Binary Pattern (LBP) features with rotation. Also, few more handcrafted are designed primarily texture based features, appearance based features, statistical based features. The experiments are performed only on MICCAI BRATS 2020 dataset.
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
Abbasi, S., Tajeripour, F.: Detection of brain tumor in 3d MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219, 526–535 (2017). https://doi.org/10.1016/j.neucom.2016.09.051, http://www.sciencedirect.com/science/article/pii/S0925231216310864
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: State of the art and future directions. J. Digital Imaging 30(4), 449–459 (2017). https://doi.org/10.1007/s10278-017-9983-4
Bakas, S., et al.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific Data 4 (09 2017). https://doi.org/10.1038/sdata.2017.117
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection, July 2017. https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection, July 2017. https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. CoRR abs/1811.02629 (2018). http://arxiv.org/abs/1811.02629
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Gordillo, N., Montseny, E., Sobrevilla, P.: State of the art survey on mri brain tumor segmentation. Magnetic Resonance Imaging 31(8), 1426–1438 (2013). https://doi.org/10.1016/j.mri.2013.05.002. http://www.sciencedirect.com/science/article/pii/S0730725X13001872
Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017). https://doi.org/10.1016/j.media.2016.05.004, http://www.sciencedirect.com/science/article/pii/S1361841516300330
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 234–244. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_21
Işin, A., Direkolu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput. Sci. 102, 317–324 (2016). https://doi.org/10.1016/j.procs.2016.09.407,http://www.sciencedirect.com/science/article/pii/S187705091632587X, 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS: 29–30 August 2016. Austria, Vienna (2016)
Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450–462. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_38
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). https://doi.org/10.1016/j.media.2016.10.004, http://www.sciencedirect.com/science/article/pii/S1361841516301839
Lefkovits, L., Lefkovits, S., Szilágyi, L.: Brain tumor segmentation with optimized random forest. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, vol. 10154, pp. 88–99. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55524-9_9
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015). https://doi.org/10.1109/TMI.2014.2377694
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016). https://doi.org/10.1109/TMI.2016.2538465
Phophalia, A., Maji, P.: Multimodal brain tumor segmentation using ensemble of forest method. In: BrainLes@MICCAI (2017)
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_28
Song, B., Chou, C.R., Chen, X., Huang, A., Liu, M.C.: Anatomy-guided brain tumor segmentation and classification. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) BrainLes 2016. LNCS, vol. 10154, pp. 162–170. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55524-9_16
Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4itk: improved n3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010). https://doi.org/10.1109/TMI.2010.2046908
Zhou, Z.H., Feng, J.: Deep forest. National Sci. Rev. 6(1), 74–86 (10 2018). https://doi.org/10.1093/nsr/nwy108. https://doi.org/10.1093/nsr/nwy108
Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O.M., Das, T., Jena, R., Price, S.J.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33454-2_46
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Shaikh, S., Phophalia, A. (2021). A Deep Random Forest Approach for Multimodal Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-72087-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72086-5
Online ISBN: 978-3-030-72087-2
eBook Packages: Computer ScienceComputer Science (R0)