Abstract
The image semantic segmentation has been extensively studying. The modern methods rely on the deep convolutional neural networks, which can be trained to address this problem. A few years ago networks require the huge dataset to be trained. However, the recent advances in deep learning allow training networks on the small datasets, which is a critical issue for medical images, since the hospitals and research organizations usually do not provide the huge amount of data. In this paper, we address medical image semantic segmentation problem by applying the modern CNN model. Moreover, the recent achievements in deep learning allow processing the whole image per time by applying concepts of the fully convolutional neural network. Our qualitative and quantitate experiment results demonstrated that modern CNN can successfully tackle the medical image semantic segmentation problem.
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Acknowledgements
This work is partially funded by the MOE—Microsoft Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, the Major State Basic Research Development Program of China (973 Program 2015CB351804), and the National Natural Science Foundation of China under Grant Nos. 61572155, 61672188, and 61572153. We would also like to acknowledge NVIDIA Corporation who kindly provided two sets of GPU.
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The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
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Jiang, F., Grigorev, A., Rho, S. et al. Medical image semantic segmentation based on deep learning. Neural Comput & Applic 29, 1257–1265 (2018). https://doi.org/10.1007/s00521-017-3158-6
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DOI: https://doi.org/10.1007/s00521-017-3158-6