Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 6
1886-1889,1894

Novel prostate MRI segmentation method based on full convolution DenseNet

Hu Xuegang
Yang Hongguang
Chongqing Key Laboratory of Signal & Information Processing, College of Information & Communication Engineering, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
DOI: 10.19734/j.issn.1001-3695.2018.11.0909

Abstract

Automatic segmentation of prostate MRI images is very important for the diagnosis of prostate diseases. However, due to the small proportion of prostate regions and fuzzy tissue boundaries, automatic segmentation has become an important challenge. This paper proposed a novel prostate MRI segmentation method based on full convolution DenseNet. Based on the popular deep learning theory, the method applied the idea of transfer learning to load DenseNet from natural images to prostate datasets. The full convolution DenseNet used deconvolutions and the connection way similar to U-net, and achieved end to end image segmentation. Furthermore, improving the Dice similarity loss function could solve the problems that the proportion of background in the prostate MRI image is much larger than that of prostate region and some pixels are difficult to be accurately identify. The experiment on PROMISE12 dataset shows that the Dice similarity coefficient is over 93.25%, and Hausdorff distance is shorter than 1.2 mm. Compared with other main methods, the proposed method is more effective and takes less time.

Foundation Support

国家自然科学基金资助项目(61571071)
重庆市自然科学基金重点资助项目(cstc2017jcyjXB0037)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.11.0909
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 6
Section: Technology of Graphic & Image
Pages: 1886-1889,1894
Serial Number: 1001-3695(2020)06-059-1886-04

Publish History

[2020-06-05] Printed Article

Cite This Article

胡学刚, 杨洪光. 基于全卷积DenseNet的前列腺MRI分割新方法 [J]. 计算机应用研究, 2020, 37 (6): 1886-1889,1894. (Hu Xuegang, Yang Hongguang. Novel prostate MRI segmentation method based on full convolution DenseNet [J]. Application Research of Computers, 2020, 37 (6): 1886-1889,1894. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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