Abstract
In this paper a new architecture of convolutional neural networks is proposed. It is a fully-convolutional architecture which allows to keep the size of the processed image constant. This, in consequence, allows to apply it for image segmentation tasks where for a given image a mask representing sought regions should be produced. An additional advantage of this architecture is its ability to learn from smaller images which reduces the amount of data that must be propagated through the network. The trained network can be still applied to images of any size. The proposed method was used for automatic localization of demyelinating plaques in head MRI sequences. This work was possible, which should be emphasized, only thanks to the manually outlined plaques provided by radiologist. To present characteristic of the considered approach three architectures and three result evaluation methods were discussed and compared.
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References
Tomczyk, A., Spurek, P., Podgórski, M., Misztal, K., Tabor, J.: Detection of elongated structures with hierarchical active partitions and CEC-based image representation. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds.) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. AISC, vol. 403, pp. 159–168. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-26227-7_15
Tomczyk, A., Szczepaniak, P.S.: Adaptive potential active contours. Pattern Anal. Appl. 14, 425–440 (2011)
de Brebisson, A., Montana, G.: Deep Neural Networks for Anatomical Brain Segmentation. ArXiv e-prints arXiv:1502.02445 (2015)
Shelhamer, E., Long, J., Darrell, T.: Fully Convolutional Networks for Semantic Segmentation. ArXiv e-prints arXiv:1605.06211 (2016)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. ArXiv e-prints arXiv:1606.04797 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv e-prints arXiv:1505.04597 (2015)
Hubel, D.H., Wiesel, T.N.: Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. J. Neurophysiol. 28, 229–289 (1965)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012)
LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995)
Cireşan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI 2011, vol. 2, pp. 1237–1242. AAAI Press (2011)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009 (2009)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. CoRR abs/1311.2901 (2013)
Nguyen, T.V., Lu, C., Sepulveda, J., Yan, S.: Adaptive nonparametric image parsing. CoRR abs/1505.01560 (2015)
Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54, 7405–7415 (2016)
Mopuri, K.R., Babu, R.V.: Object level deep feature pooling for compact image representation. CoRR abs/1504.06591 (2015)
Matsugu, M., Mori, K., Mitari, Y., Kaneda, Y.: Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw. 16, 555–559 (2003)
Dai, J., He, K., Sun, J.: Convolutional feature masking for joint object and stuff segmentation. CoRR abs/1412.1283 (2014)
Stasiak, B., Tarasiuk, P., Michalska, I., Tomczyk, A., Szczepaniak, P.: Localization of demyelinating plaques in MRI using convolutional neural networks. In: Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), BIOIMAGING, vol. 2, pp. 55–64. SCITEPRESS (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. CoRR abs/1502.01852 (2015)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp. 2278–2324 (1998)
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)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Acknowledgements
This project has been partly funded with support from National Science Centre, Republic of Poland, decision number DEC-2012/05/D/ST6/03091.
Authors would like to express their gratitude to the Department of Radiology of Barlicki University Hospital in Lodz for making head MRI sequences available.
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Stasiak, B., Tarasiuk, P., Michalska, I., Tomczyk, A., Szczepaniak, P.S. (2018). Convolutional Neural Network Based Segmentation of Demyelinating Plaques in MRI. In: Peixoto, N., Silveira, M., Ali, H., Maciel, C., van den Broek, E. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2017. Communications in Computer and Information Science, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-319-94806-5_10
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