Automatic Segmentation of Breast and Fibroglandular Tissues in DCE-MR Images Based on nnU-Net

doi:10.11938/cjmr20212883

Abstract

Abstract:

Segmentation of whole breast and fibroglandular tissue (FGT) is an important task for quantitative analysis of breast cancer risk in dynamic contrast enhanced magnetic resonance (DCE-MR) images. In this study, an automated segmentation model based on nnU-Net is proposed to segment the whole breast and FGT in 3D fat-suppressed breast DCE-MR images, taking the advantages of hierarchical image features learning, as well as the fusion of deep features and shallow features. The model could automatically perform preprocessing, data augmentation and dynamic adaptation of network configurations with respect to different imaging parameters. Experimental results show that the method could accurately and efficiently segment the whole breast and FGT in the collected dataset of 3D fat-suppressed breast DCE-MR images with variable imaging characteristics, achieving the average Dice similarity coefficients 0.969±0.007 and 0.893±0.054, respectively, for breast and FGT segmentation.

Key words: breast dynamic contrast enhanced magnetic resonance image, breast segmentation, fibroglandular tissue segmentation, deep learning, nnU-Net model

CLC Number:

TP391.4

Lu HUO,Xiao-xin HU,Qin XIAO,Ya-jia GU,Xu CHU,Luan JIANG. Automatic Segmentation of Breast and Fibroglandular Tissues in DCE-MR Images Based on nnU-Net[J]. Chinese Journal of Magnetic Resonance, 2021, 38(3): 367-380.

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	DSC	Acc	Sen	Spec	ASD/mm
乳房分割	0.969±0.007	0.995±0.006	0.961±0.027	0.994±0.002	0.181±0.032
腺体分割	0.893±0.054	0.997±0.003	0.926±0.017	0.998±0.002	0.240±0.021

	乳房分割	腺体分割
组1	0.973±0.011	0.903±0.055
组2	0.977±0.026	0.946±0.042

	乳房分割	腺体分割
Ⅰ类	0.989±0.004	0.906±0.048
Ⅱ类	0.980±0.008	0.951±0.021
Ⅲ类	0.941±0.029	0.917±0.031
Ⅳ类	0.956±0.010	0.868±0.037

		乳房分割	腺体分割
验证集	Fold 1	0.970±0.049	0.941±0.061
	Fold 2	0.982±0.014	0.945±0.046
	Fold 3	0.971±0.044	0.942±0.047
	Fold 4	0.982±0.016	0.946±0.052
	Fold 5	0.975±0.012	0.942±0.031
	平均值±标准差	0.976±0.027	0.944±0.047
测试集		0.969±0.007	0.893±0.054

方法	测试样本数	乳房分割	腺体分割
文献[5]	27	0.94±0.03	0.80±0.13
文献[6]	37	0.96±0.02	0.83±0.06
文献[18]	28	0.86±0.05	0.83±0.06
文献[19]	22	0.94±0.03	0.81±0.11
本文方法	40	0.97±0.01	0.89±0.05