Magnetic Resonance Images Segmentation of Synovium Based on Dense-UNet++

doi:10.11938/cjmr20212905

Abstract

Abstract:

To further improve the segmentation accuracy, robustness, and training efficiency of existing articular synovium segmentation algorithms, a new deep learning network based on Dense-UNet++ was proposed. First, we inserted the DenseNet module into the UNet++ network, then applied the Swish activation function to train the model. The network was trained through 14 512 synovial images augmented from 1 036 synovial images, and tested through 68 images. The average accuracy of the model reached 0.819 9 for dice similarity coefficient (DSC), and 0.927 9 for intersection over union (IOU) index. Compared with UNet, ResUNet and VGG-UNet++, DSC coefficient and IOU index were improved, and DSC oscillation coefficient reduced. In addition, when applied in the same synovial image set and using the same network structure, the Swish function can help improve the accuracy of segmentation compared with the ReLu function. The experimental results show that the proposed algorithm performs better in segmenting articular synovium and may assist doctors in disease diagnosis.

Key words: magnetic resonance imaging (MRI), deep learning, medical image segmentation, synovitis

CLC Number:

O482.53

Zhen-yu WANG, Ying-shan WANG, Jin-ling MAO, Wei-wei MA, Qing LU, Jie SHI, Hong-zhi WANG. Magnetic Resonance Images Segmentation of Synovium Based on Dense-UNet++[J]. Chinese Journal of Magnetic Resonance, 2022, 39(2): 208-219.

Figures/Tables 11

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网络结构	激活函数	Batch size	平均DSC	平均IOU	训练耗时
UNet	ReLu	8	0.4432	0.5109	47 h
UNet	Swish	8	0.5369	0.6495	22 h

网络结构	激活函数	Batch size	平均DSC	平均IOU	DSC标准差
ResUNet^[17]	ReLu	8	0.5832	0.7990	/
VGG-UNet++	ReLu	8	0.7399	0.8136	0.049
Dense-UNet++	ReLu	8	0.7793	0.8447	0.038
Dense-UNet++	Swish	8	0.8199	0.9279	0.046

网络结构	激活函数	是否使用密集模块	DSC曲线振荡系数
UNet	ReLu	否	2.534×10^-4
UNet	Swish	否	1.439×10^-4
ResUNet	ReLu	否	3.295×10^-4
VGG-UNet++	ReLu	否	2.321×10^-4
Dense-UNet++	ReLu	是	9.412×10^-5
Dense-UNet++（本文方法）	Swish	是	2.835×10^-5

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