Abstract
We introduce a modification of our previous 3D-to-2D fully convolutional architecture, DeepSCAN, replacing batch normalization with instance normalization, and adding a lightweight local attention mechanism. These networks are trained using a previously described loss function which mo els label noise and uncertainty. We present results on the validation dataset of the Multimodal Brain Tumor Segmentation Challenge 2019.
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Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 170117 (2017)
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. (2017)
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017)
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge (2018). ArXiv abs/1811.02629
Bauer, S., Wiest, R., Nolte, L.L., Reyes, M.: A survey of mri-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97–129 (2013)
BenTaieb, A., Hamarneh, G.: Uncertainty driven multi-loss fully convolutional networks for histopathology. In: Cardoso, M.J., et al. (eds.) LABELS/CVII/STENT -2017. LNCS, vol. 10552, pp. 155–163. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67534-3_17
Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Jungo, A., et al.: On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 682–690. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_77
Jungo, A., Meier, R., Ermis, E., Herrmann, E., Reyes, M.: Uncertainty-driven sanity check: application to postoperative brain tumor cavity segmentation. In: Proceedings of MIDL (2018)
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)
Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: NIPS (2017)
Lin, T.Y., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999–3007 (2017)
Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: ICLR (2017)
McClure, P., et al.: Knowing what you know in brain segmentation using deep neural networks (2018). http://arxiv.org/abs/1812.01719
McKinley, R., Meier, R., Wiest, R.: Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 456–465. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_40
McKinley, R., Rebsamen, M., Meier, R., Reyes, M., Rummel, C., Wiest, R.: Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks (2019). arXiv e-print, https://arxiv.org/abs/1904.02436
McKinley, R., et al.: Simultaneous lesion and neuroanatomy segmentation in multiple sclerosis using deep neural networks (2019). ArXiv abs/1901.07419
Menze, B.H., Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Nair, T., et al.: Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. In: Proceedings of MICCAI (2018)
Nix, D.A., Weigend, A.S.: Estimating the mean and variance of the target probability distribution. In: IEEE ICNN 1994, vol. 1 (1994)
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)
Roy, A.G., Conjeti, S., Navab, N., Wachinger, C.: Inherent brain segmentation quality control from fully convnet monte carlo sampling. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 664–672. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_75
Szegedy, C., et al.: Rethinking the Inception architecture for computer vision. In: CVPR (2016)
Wang, G., et al.: Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338, 34–45 (2019)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: Proceedings of International Conference on Learning Representations (ICLR 2017) (2017)
Acknowledgements
This work was supported by the Swiss Personalized Health Network (SPHN, project number 2018DRI10). Calculations were performed on UBELIX (http://www.id.unibe.ch/hpc), the HPC cluster at the University of Bern.
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McKinley, R., Rebsamen, M., Meier, R., Wiest, R. (2020). Triplanar Ensemble of 3D-to-2D CNNs with Label-Uncertainty for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11992. Springer, Cham. https://doi.org/10.1007/978-3-030-46640-4_36
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