Magnetic Resonance Imaging Brain Tumor Image Segmentation Based on Improved U-Net

Haiwei Mu; Ying Guo; Xinghui Quan; Zhimin Cao; Jian Han

doi:10.3788/LOP202158.0410022

Laser & Optoelectronics Progress, Volume. 58, Issue 4, 0410022(2021)

Magnetic Resonance Imaging Brain Tumor Image Segmentation Based on Improved U-Net

Haiwei Mu^1,2, Ying Guo^1,2, Xinghui Quan^1,2、*, Zhimin Cao^1,2, and Jian Han^1,2

Author Affiliations

¹School of Physics and Electrical Engineering, Northeast Petroleum University, Daqing, Heilongjiang 163318, China

²Research and Development Center for Testing and Measurement Technology and Instrumentation, Heilongjiang Province Universities, Northeast Petroleum University, Daqing, Heilongjiang 163318, China

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Figures & Tables(8)

Fig. 1. Structure diagram of the dense jump connection

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Fig. 2. Structure diagram of the improved residual module

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Fig. 3. Structure of the FRN layer

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Fig. 4. Structure of the improved network

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Fig. 5. MRI image of brain tumor. (a) FLAIR; (b) T1; (c) T1ce; (d) T2; (e) GT

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Fig. 6. Segmentation results of MRI images by different algorithms. (a) Patient1--(g) patient7

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Table 1. Tumor segmentation results of the 4 models

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Table 1. Tumor segmentation results of the 4 models

Algorithm	Dice			PPV			Sensitivity			Hausdorff
Algorithm	WT	TC	ET	WT	TC	ET	WT	TC	ET	WT	TC	ET
FCN8	0.81	0.80	0.70	0.79	0.82	0.69	0.85	0.88	0.75	2.98	1.85	3.20
U-Net	0.83	0.81	0.76	0.85	0.84	0.77	0.86	0.89	0.82	2.66	1.75	2.84
ResNet	0.84	0.82	0.77	0.86	0.85	0.79	0.86	0.90	0.82	2.63	1.72	2.81
Ours	0.88	0.84	0.80	0.90	0.86	0.82	0.89	0.91	0.84	2.52	1.51	2.66

Table 2. Tumor segmentation results of different algorithms

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Table 2. Tumor segmentation results of different algorithms

Algorithm	Dice			Hausdorff
Algorithm	WT	TC	ET	WT	TC	ET
Ref. [27]	0.7385	0.6931	0.5772	3.4852	2.2848	3.5821
Ref. [28]	0.8106	0.7823	0.7017	3.0646	2.0548	3.1425
Ref. [29]	0.8335	0.8051	0.7634	2.8368	1.8267	2.8459
Ours	0.8843	0.8402	0.8098	2.5252	1.5144	2.6616

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Haiwei Mu, Ying Guo, Xinghui Quan, Zhimin Cao, Jian Han. Magnetic Resonance Imaging Brain Tumor Image Segmentation Based on Improved U-Net[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410022

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Paper Information

Category: Image Processing

Received: Jul. 17, 2020

Accepted: Aug. 13, 2020

Published Online: Feb. 24, 2021

The Author Email: Quan Xinghui (xinghui8@126.com)

DOI:10.3788/LOP202158.0410022

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology