Abstract
Uncertainty measures of medical image analysis technologies, such as deep learning, are expected to facilitate their clinical acceptance and synergies with human expertise. Therefore, we propose a full-resolution residual convolutional neural network (FRRN) for brain tumor segmentation and examine the principle of Monte Carlo (MC) Dropout for uncertainty quantification by focusing on the Dropout position and rate. We further feed the resulting brain tumor segmentation into a survival prediction model, which is built on age and a subset of 26 image-derived geometrical features such as volume, volume ratios, surface, surface irregularity and statistics of the enhancing tumor rim width. The results show comparable segmentation performance between MC Dropout models and a standard weight scaling Dropout model. A qualitative evaluation further suggests that informative uncertainty can be obtained by applying MC Dropout after each convolution layer. For survival prediction, results suggest only using few features besides age. In the BraTS17 challenge, our method achieved the 2nd place in the survival task and completed the segmentation task in the 3rd best-performing cluster of statistically different approaches.
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References
Alberts, E., Rempfler, M., Alber, G., Huber, T., Kirschke, J., Zimmer, C., Menze, B.H.: Uncertainty quantification in brain tumor segmentation using CRFs and random perturbation models. In: Proceedings - International Symposium on Biomedical Imaging, vol. 2016 June, pp. 428–431. IEEE, April 2016
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 4, 170117 (2017)
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The Cancer Imaging Archive, January 2017. https://wiki.cancerimagingarchive.net/display/DOI/Segmentation+Labels+and+Radiomic+Features+for+the+Pre-operative+Scans+of+the+TCGA-LGG+collection
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive, January 2017. https://wiki.cancerimagingarchive.net/display/DOI/Segmentation+Labels+and+Radiomic+Features+for+the+Pre-operative+Scans+of+the+TCGA-GBM+collection;jsessionid=C2BE9FB8F9D5532DCA9E5CD294787DBC
Cui, Y., Tha, K.K., Terasaka, S., Yamaguchi, S., Wang, J., Kudo, K., Xing, L., Shirato, H., Li, R.: Prognostic imaging biomarkers in glioblastoma: development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology 278(2), 546–553 (2016)
Czarnek, N., Clark, K., Peters, K.B., Mazurowski, M.A.: Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study. J. Neurooncol. 132(1), 55–62 (2017)
Gal, Y.: Uncertainty in Deep Learning. Ph.D. thesis, University of Cambridge (2016)
Gal, Y., Ghahramani, Z.: Bayesian convolutional neural networks with Bernoulli approximate variational inference, June 2015. http://arxiv.org/abs/1506.02158
Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: images are more than pictures, they are data. Radiology 278(2), 563–577 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian SegNet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. In: Proceedings of the British Machine Vision Conference (BMVC) (2017)
Kickingereder, P., Neuberger, U., Bonekamp, D., Piechotta, P.L., Götz, M., Wick, A., Sill, M., Kratz, A., Shinohara, R.T., Jones, D.T.W., Radbruch, A., Muschelli, J., Unterberg, A., Debus, J., Schlemmer, H.P., Herold-Mende, C., Pfister, S., von Deimling, A., Wick, W., Capper, D., Maier-Hein, K.H., Bendszus, M.: Radiomic subtyping improves disease stratification beyond key molecular, clinical and standard imaging characteristics in patients with glioblastoma. Neuro-Oncology, nox188 (2017)
Lê, M., Unkelbach, J., Ayache, N., Delingette, H.: GPSSI: gaussian process for sampling segmentations of images. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 38–46. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_5
Meier, R., Knecht, U., Jungo, A., Wiest, R., Reyes, M.: Perturb-and-MPM: quantifying segmentation uncertainty in dense multi-label CRFs, March 2017. http://arxiv.org/abs/1703.00312
Menze, B., 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., Ayache, N., Buendia, P., Collins, L., Cordier, N., Corso, J., Criminisi, A., Das, T., Delingette, H., Demiralp, C., Durst, C., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K., Jena, R., John, N., Konukoglu, E., Lashkari, D., Antonio Mariz, J., Meier, R., Pereira, S., Precup, D., Price, S.J., Riklin-Raviv, T., Reza, S., Ryan, M., Schwartz, L., Shin, H.C., Shotton, J., Silva, C., Sousa, N., Subbanna, N., Szekely, G., Taylor, T., Thomas, O., Tustison, N., Unal, G., Vasseur, F., Wintermark, M., Hye Ye, D., 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, 33 (2014)
Pérez-Beteta, J., Martínez-González, A., Molina, D., Amo-Salas, M., Luque, B., Arregui, E., Calvo, M., Borrás, J.M., López, C., Claramonte, M., Barcia, J.A., Iglesias, L., Avecillas, J., Albillo, D., Navarro, M., Villanueva, J.M., Paniagua, J.C., Martino, J., Velásquez, C., Asenjo, B., Benavides, M., Herruzo, I., Delgado, M.D.C., del Valle, A., Falkov, A., Schucht, P., Arana, E., Pérez-Romasanta, L., Pérez-García, V.M.: Glioblastoma: does the pre-treatment geometry matter? a postcontrast T1 MRI-based study. Eur. Radiol. 27(3), 1096–1104 (2017)
Pohlen, T., Hermans, A., Mathias, M., Leibe, B.: Full-resolution residual networks for semantic segmentation in street scenes. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Velazquez, R.E., Meier, R., Dunn, W.D., Alexander, B., Wiest, R., Bauer, S., Gutman, D.A., Reyes, M., Aerts, H.J.W.L.: Fully automatic GBM segmentation in the TCGA-GBM dataset: prognosis and correlation with VASARI features. Scientific reports 5, 16822, November 2015
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Yip, S.S.F., Aerts, H.J.W.L.: Applications and limitations of radiomics. Phy. Med. Biol. 61(13), R150–R166 (2016)
Acknowledgments
This work was supported by the Swiss National Foundation by grant number 169607, the Swiss Cancer League project MANAGE KFS-3979-08-2016, the Spanish Ministerio de Economía y Competitividad/FEDER by grant number MTM2015-71200-R, and the James S. Mc. Donnell Foundation 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer [Special Initiative Collaborative – Planning Grant 220020420 and Collaborative award 220020450].
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Jungo, A. et al. (2018). Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_40
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