Localization of Demyelinating Plaques in MRI using Convolutional Neural Networks

Bartłomiej Stasiak, Pawel Tarasiuk, Izabela Michalska, Arkadiusz Tomczyk, Piotr S. Szczepaniak

2017

Abstract

In the paper a method of demyelinating plaques localization in head MRI sequences is presented. For that purpose a convolutional neural network is used. It is trained to act as non-linear filter, which should indicate (give a high response) in those image areas where the sought objects are located. Consequently, the output of the proposed architecture is an image and not a single label as it is in the case of traditional networks with pooling and fully connected layers. Another interesting feature of the proposed solution is the ability to select network parameters using smaller patches cut from training images which reduces the amount of data that must be propagated through the network. It should be emphasized that the conducted research was possible only thanks to the manually outlined plaques provided by radiologist.

References

  1. Cheng, G., Zhou, P., and Han, J. (2016). Learning rotationinvariant convolutional neural networks for object detection in vhr optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54(12):7405-7415.
  2. Cires¸an, D. C., Meier, U., Masci, J., Gambardella, L. M., and Schmidhuber, J. (2011). Flexible, high performance convolutional neural networks for image classification. InProceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume Two, IJCAI'11, pages 1237-1242.
  3. Dai, J., He, K., and Sun, J. (2014). Convolutional feature masking for joint object and stuff segmentation. CoRR, abs/1412.1283.
  4. de Brebisson, A. and Montana, G. (2015). Deep Neural Networks for Anatomical Brain Segmentation. ArXiv e-prints, 1502.02445.
  5. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and FeiFei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.
  6. He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. CoRR, abs/1502.01852.
  7. Hubel, D. H. and Wiesel, T. N. (1965). Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. Journal of Neurophysiology, 28:229-289.
  8. Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 25, pages 1097- 1105. Curran Associates, Inc.
  9. LeCun, Y. and Bengio, Y. (1995). Convolutional networks for images, speech, and time-series. In Arbib, M. A., editor, The Handbook of Brain Theory and Neural Networks. MIT Press.
  10. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. In Proceedings of the IEEE, pages 2278-2324.
  11. Matsugu, M., Mori, K., Mitari, Y., and Kaneda, Y. (2003). Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Networks, 16(5-6):555-559.
  12. Milletari, F., Navab, N., and Ahmadi, S.-A. (2016). VNet: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. ArXiv e-prints, 1606.04797.
  13. Mopuri, K. R. and Babu, R. V. (2015). Object level deep feature pooling for compact image representation. CoRR, abs/1504.06591.
  14. Nguyen, T. V., Lu, C., Sepulveda, J., and Yan, S. (2015). Adaptive nonparametric image parsing. CoRR, abs/1505.01560.
  15. Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv e-prints, 1505.04597.
  16. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. CoRR, abs/1312.6229.
  17. Shelhamer, E., Long, J., and Darrell, T. (2016). Fully Convolutional Networks for Semantic Segmentation. ArXiv e-prints, 1605.06211.
  18. Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556.
  19. Zeiler, M. D. and Fergus, R. (2013). Visualizing and understanding convolutional networks. CoRR, abs/1311.2901.
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Paper Citation


in Harvard Style

Stasiak B., Tarasiuk P., Michalska I., Tomczyk A. and S. Szczepaniak P. (2017). Localization of Demyelinating Plaques in MRI using Convolutional Neural Networks . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017) ISBN 978-989-758-215-8, pages 55-64. DOI: 10.5220/0006298200550064


in Bibtex Style

@conference{bioimaging17,
author={Bartłomiej Stasiak and Pawel Tarasiuk and Izabela Michalska and Arkadiusz Tomczyk and Piotr S. Szczepaniak},
title={Localization of Demyelinating Plaques in MRI using Convolutional Neural Networks},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)},
year={2017},
pages={55-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006298200550064},
isbn={978-989-758-215-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)
TI - Localization of Demyelinating Plaques in MRI using Convolutional Neural Networks
SN - 978-989-758-215-8
AU - Stasiak B.
AU - Tarasiuk P.
AU - Michalska I.
AU - Tomczyk A.
AU - S. Szczepaniak P.
PY - 2017
SP - 55
EP - 64
DO - 10.5220/0006298200550064