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2016 | OriginalPaper | Chapter

Lifted Auto-Context Forests for Brain Tumour Segmentation

Authors: Loic Le Folgoc, Aditya V. Nori, Siddharth Ancha, Antonio Criminisi

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

Publisher: Springer International Publishing

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Abstract

We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on hold-out estimates, (2) increased compactness at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster, and (3) guided semantic bagging that exposes latent data-space semantics captured by forest pathways. The proposed framework is practical: the per-layer training is fast, modular and robust. It was a top performer in the MICCAI 2016 BRATS (Brain Tumour Segmentation) challenge, and this paper aims to discuss and provide details about the challenge entry.
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Footnotes
1
As an illustration on WT layers. The 4 (WT) clusters are obtained from (the single DF of) the first WT layer. The second and third WT layers each consist of 4 distinct (50-tree) DFs, each of which is trained on cluster-specific data. At test time, voxels \(\varvec{\mathrm {x}}\) pass through the first WT layer and are assigned a cluster \(k_{\varvec{\mathrm {x}}}\!\in \!\{1\cdots 4\}\). Then for the second and third layers, they are sent through the DF specific to the \(k_{\varvec{\mathrm {x}}}\)-th cluster. The same process is followed for TC and ET layers.
 
Literature
1.
go back to reference Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997) CrossRef Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997) CrossRef
2.
go back to reference Archambeau, C., Verleysen, M.: Robust Bayesian clustering. Neural Netw. 20(1), 129–138 (2007) CrossRefMATH Archambeau, C., Verleysen, M.: Robust Bayesian clustering. Neural Netw. 20(1), 129–138 (2007) CrossRefMATH
3.
go back to reference Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996) MATH Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996) MATH
4.
go back to reference Cordier, N., Delingette, H., Ayache, N.: A patch-based approach for the segmentation of pathologies: application to glioma labelling. IEEE Trans. Med. Imaging 35(4), 1066–1076 (2015) CrossRef Cordier, N., Delingette, H., Ayache, N.: A patch-based approach for the segmentation of pathologies: application to glioma labelling. IEEE Trans. Med. Imaging 35(4), 1066–1076 (2015) CrossRef
5.
go back to reference Criminisi, A., Robertson, D., Konukoglu, E., Shotton, J., Pathak, S., White, S., Siddiqui, K.: Regression forests for efficient anatomy detection and localization in computed tomography scans. Med. Image Anal. 17(8), 1293–1303 (2013) CrossRef Criminisi, A., Robertson, D., Konukoglu, E., Shotton, J., Pathak, S., White, S., Siddiqui, K.: Regression forests for efficient anatomy detection and localization in computed tomography scans. Med. Image Anal. 17(8), 1293–1303 (2013) CrossRef
6.
go back to reference Geremia, E., Menze, B.H., Ayache, N.: Spatially adaptive random forests. In: 2013 IEEE 10th International Symposium on Biomedical Imaging, pp. 1344–1347. IEEE (2013) Geremia, E., Menze, B.H., Ayache, N.: Spatially adaptive random forests. In: 2013 IEEE 10th International Symposium on Biomedical Imaging, pp. 1344–1347. IEEE (2013)
7.
go back to reference Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998) CrossRef Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998) CrossRef
8.
go back to reference Kamnitsas, K., Ledig, C., Newcombe, V.F.J., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017). Elsevier CrossRef Kamnitsas, K., Ledig, C., Newcombe, V.F.J., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017). Elsevier CrossRef
9.
go back to reference Menze, B., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015) CrossRef Menze, B., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015) CrossRef
10.
go back to reference Menze, B.H., Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., Golland, P.: A generative model for brain tumor segmentation in multi-modal images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 151–159. Springer, Heidelberg (2010). doi: 10.​1007/​978-3-642-15745-5_​19 CrossRef Menze, B.H., Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., Golland, P.: A generative model for brain tumor segmentation in multi-modal images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 151–159. Springer, Heidelberg (2010). doi: 10.​1007/​978-3-642-15745-5_​19 CrossRef
11.
go back to reference Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 131–143. Springer, Cham (2016). doi: 10.​1007/​978-3-319-30858-6_​12 CrossRef Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 131–143. Springer, Cham (2016). doi: 10.​1007/​978-3-319-30858-6_​12 CrossRef
12.
go back to reference Quinlan, J.R.: Simplifying decision trees. Int. J. Man. Mach. Stud. 27(3), 221–234 (1987) CrossRef Quinlan, J.R.: Simplifying decision trees. Int. J. Man. Mach. Stud. 27(3), 221–234 (1987) CrossRef
13.
go back to reference Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008) Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)
14.
go back to reference Tu, Z.: Auto-context and its application to high-level vision tasks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008) Tu, Z.: Auto-context and its application to high-level vision tasks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)
15.
go back to reference Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1744–1757 (2010) CrossRef Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1744–1757 (2010) CrossRef
17.
go back to reference Tustison, N.J., Shrinidhi, K., Wintermark, M., Durst, C.R., Kandel, B.M., Gee, J.C., Grossman, M.C., Avants, B.B.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13(2), 209–225 (2015) CrossRef Tustison, N.J., Shrinidhi, K., Wintermark, M., Durst, C.R., Kandel, B.M., Gee, J.C., Grossman, M.C., Avants, B.B.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13(2), 209–225 (2015) CrossRef
18.
go back to reference Tustison, N., Wintermark, M., Durst, C., Avants, B.: Ants and arboles. Multimodal Brain Tumor Segmentation, p. 47 (2013) Tustison, N., Wintermark, M., Durst, C., Avants, B.: Ants and arboles. Multimodal Brain Tumor Segmentation, p. 47 (2013)
19.
go back to reference Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. I-511. IEEE (2001) Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. I-511. IEEE (2001)
20.
go back to reference Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov Random Field model and the Expectation-Maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001) CrossRef Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov Random Field model and the Expectation-Maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001) CrossRef
21.
go back to reference Zikic, D., et al.: 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). doi: 10.​1007/​978-3-642-33454-2_​46 CrossRef Zikic, D., et al.: 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). doi: 10.​1007/​978-3-642-33454-2_​46 CrossRef
Metadata
Title
Lifted Auto-Context Forests for Brain Tumour Segmentation
Authors
Loic Le Folgoc
Aditya V. Nori
Siddharth Ancha
Antonio Criminisi
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-55524-9_17

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