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15.07.2017 | Neural Computing in Next Generation Virtual Reality Technology

Medical image semantic segmentation based on deep learning

verfasst von: Feng Jiang, Aleksei Grigorev, Seungmin Rho, Zhihong Tian, YunSheng Fu, Worku Jifara, Khan Adil, Shaohui Liu

Erschienen in: Neural Computing and Applications | Ausgabe 5/2018

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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.

Vorheriger Artikel An overview, examples, and impacts offered by Emerging Services and Analytics in Cloud Computing virtual reality

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Titel: Medical image semantic segmentation based on deep learning
verfasst von: Feng Jiang
Aleksei Grigorev
Seungmin Rho
Zhihong Tian
YunSheng Fu
Worku Jifara
Khan Adil
Shaohui Liu
Publikationsdatum: 15.07.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-3158-6

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