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Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Brain Tumor Segmentation in MRI Scans Using Deeply-Supervised Neural Networks

Authors : Reza Pourreza, Ying Zhuge, Holly Ning, Robert Miller

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

Publisher: Springer International Publishing

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Abstract

Gliomas are the most frequent primary brain tumors in adults. Improved quantification of the various aspects of a glioma requires accurate segmentation of the tumor in magnetic resonance images (MRI). Since the manual segmentation is time-consuming and subject to human error and irreproducibility, automatic segmentation has received a lot of attention in recent years. This paper presents a fully automated segmentation method which is capable of automatic segmentation of brain tumor from multi-modal MRI scans. The proposed method is comprised of a deeply-supervised neural network based on Holistically-Nested Edge Detection (HED) network. The HED method, which is originally developed for the binary classification task of image edge detection, is extended for multiple-class segmentation. The classes of interest include the whole tumor, tumor core, and enhancing tumor. The dataset provided by 2017 Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) challenge is used in this work for training the neural network and performance evaluations. Experiments on BraTS 2017 challenge datasets demonstrate that the method performs well compared to the existing works. The assessments revealed the Dice scores of 0.86, 0.60, and 0.69 for whole tumor, tumor core, and enhancing tumor classes, respectively.

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previous chapter Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network
next chapter Brain Tumor Segmentation and Parsing on MRIs Using Multiresolution Neural Networks
Literature
1.
Holland, E.C.: Progenitor cells and glioma formation. Curr. Opin. Neurol. 14(6), 683–688 (2001)CrossRef
2.
Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35, 1240–1251 (2016)CrossRef
3.
Njeh, I., Sallemi, L., Ayed, I.B., et al.: 3D multimodal MRI brain glioma tumor and edema segmentation: a graph cut distribution matching approach. Comput. Med. Imaging Graph. 40, 108–119 (2015)CrossRef
4.
Hamamci, A., Kucuk, N., Karaman, K., Engin, K., Unal, G.: Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans. Med. Imaging 31, 790–804 (2012)CrossRef
5.
Raviv, T.R., Van Leemput, K., Menze, B.H., Wells 3rd, W.M., Golland, P.: Segmentation of image ensembles via latent atlases. Med. Image Anal. 14, 654–665 (2010)CrossRef
6.
Guo, X.G., Schwartz, L., Zhao, B.: Semi-automatic segmentation of multimodal brain tumor using active contours. In: Medical Image Computing and Computer Assisted Intervention, pp. 27–30 (2013)
7.
Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8, 275–283 (2004)CrossRef
8.
Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., Gerig, G.: Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad. Radiol. 10, 1341–1348 (2003)CrossRef
9.
Cuadra, M.B., Pollo, C., Bardera, A., Cuisenaire, O., Villemure, J.G., Thiran, J.P.: Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Trans. Med. Imaging 23, 1301–1314 (2004)CrossRef
10.
Mohamed, A., Zacharaki, E.I., Shen, D., Davatzikos, C.: Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. Med. Image Anal. 10, 752–763 (2006)CrossRef
11.
Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q., Zhu, Y.: Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Comput. Vis. Image Underst. 115, 256–259 (2011)CrossRef
12.
13.
Tustison, N.J., Shrinidhi, K.L., Wintermark, M., et al.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13, 209–225 (2015)CrossRef
14.
Pinto, A., Pereira, S., Correia, H., Oliveira, J., Rasteiro, D.M., Silva, C.A.: Brain tumour segmentation based on extremely randomized forest with highlevel features. In: Conference Proceedings IEEE Engineering in Medicine and Biology Society 2015, pp. 3037–3040 (2015)
15.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRef
16.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 1, 1097–1115 (2012)
17.
Zhao, L., Jia, K.: Multiscale CNNs for brain tumor segmentation and diagnosis. Comput. Math. Methods Med. 2016, 8356291–8356297 (2016)CrossRef
18.
Havaei, M., Dutil, F., Pal, C., Larochelle, H., Jodoin, P.-M.: A convolutional neural network approach to brain tumor segmentation. In: Proceeding of the Multimodal Brain Tumor Segmentation Challenge, pp. 29–33 (2015)
19.
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013)CrossRef
20.
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.A., Arbel, T., Avants, B.B., Ayache, N., Buendia, P., Collins, D.L., Cordier, N., Corso, J.J., Criminisi, A., Das, T., Delingette, H., Demiralp, Ç., Durst, C.R., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K.M., Jena, R., John, N.M., Konukoglu, E., Lashkari, D., Mariz, J.A., Meier, R., Pereira, S., Precup, D., Price, S.J., Raviv, T.R., Reza, S.M., Ryan, M., Sarikaya, D., Schwartz, L., Shin, H.C., Shotton, J., Silva, C.A., Sousa, N., Subbanna, N.K., Szekely, G., Taylor, T.J., Thomas, O.M., Tustison, N.J., Unal, G., Vasseur, F., Wintermark, M., Ye, D.H., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., Van Leemput, K.: The multimodal brain tumor image segmentation benchmark (BraTS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRef
21.
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)
22.
23.
24.
Urban, G., Bendszus, M., Hamprecht, F., Kleesiek, J.: Multi-modal brain tumor segmentation using deep convolutional neural networks. In: Proceeding of the Multimodal Brain Tumor Segmentation Challenge, pp. 31–35 (2014)
25.
Davy, A., Havaei, M., Warde-farley, D., et al.: Brain tumor segmentation with deep neural networks. In: Proceeding of the Multimodal Brain Tumor Segmentation Challenge, pp. 1–5 (2014)
26.
Rao, V., Sarabi, M.S., Jaiswal, A.: Brain tumor segmentation with deep learning. In: Proceeding of the Multimodal Brain Tumor Segmentation Challenge, pp. 56–59 (2015)
27.
Lun, T.K., Hsu, W.: Brain tumor segmentation using deep convolutional neural network. In: Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, pp. 26–29 (2016)
28.
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: Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, pp. 77–80 (2016)
29.
Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Proceedings of AISTATS, pp. 562–570 (2015)
30.
Xie, S., Tu, Z.: Holistically-nested edge detection. Int. J. Comput. Vision, 1–16 (2017)
31.
Zhuge, Y., Krauze, A.V., Ning, H., Cheng, J.C., Arora, B.C., Camphausen, K., Miller, R.W.: Brain tumor segmentation using holistically nested neural networks in MRI images. Med. Phys. 44, 5234–5243 (2017)CrossRef
32.
Guillemaud, R., Brady, M.: Estimating the bias field of MR images. IEEE Trans. Med. Imaging 16, 238–251 (1997)CrossRef
33.
Tustison, N.J., Avants, B.B., Cook, P.A., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)CrossRef
34.
Zhuge, Y., Udupa, J.K.: Intensity standardization simplifies brain MR image segmentation. Comput. Vis. Image Underst. 113, 1095–1103 (2009)CrossRef
35.
Nyul, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19, 143–150 (2000)CrossRef
Metadata
Title
Brain Tumor Segmentation in MRI Scans Using Deeply-Supervised Neural Networks
Authors
Reza Pourreza
Ying Zhuge
Holly Ning
Robert Miller
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_28

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