基于全卷积网络的乳腺肿瘤动态增强磁共振图像分割

doi:10.11938/cjmr20212921

摘要/Abstract

摘要：

准确可靠的乳腺肿瘤分割是乳腺癌诊断、治疗、预后评估的关键.针对现有的基于动态增强磁共振成像（DCE-MRI）的乳腺肿瘤分割方法易遗漏小目标肿瘤等不足，本文提出了一种基于全卷积网络的可靠高效的乳腺肿瘤DCE-MRI图像分割方法.首先，对乳腺DCE-MRI数据进行预处理后，截取128*128大小的图像块，并以肿瘤区域像素数为依据将数据分为两个子数据集；其次，利用数据集训练CBP5-Net得到分类模型；然后，利用两个子数据集分别训练RAU-Net得到两个分割模型；最后，将测试集数据送到网络输入端，并对网络输出结果进行后处理，得到最终的乳腺肿瘤分割结果.利用本文提出的方法得到的Dice系数、敏感性、特异性和交并比（IoU）分别达到了0.938 8、0.952 3、0.998 5和0.876 8，说明利用本文方法能够有效、精确地分割乳腺肿瘤DCE-MRI图像.

关键词: 全卷积网络（FCN）, 图像分割, 乳腺肿瘤, 动态对比增强磁共振成像（DCE-MRI）

Abstract:

Accurate and reliable breast tumor segmentation is essential for the diagnosis, treatment and prognosis of breast cancer. To address the shortcomings of existing dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI)-based breast tumor segmentation methods, which tend to miss small tumors, we proposed a more reliable and efficient segmentation method for breast tumors in DCE-MRI based on a fully convolutional network (FCN). Firstly, the breast DCE-MRI data was preprocessed, followed by intercepting the image blocks of 128*128, and dividing the dataset into two sub-datasets according to the number of pixels in the tumor region. Secondly, the whole set was used to train CBP5-Net to obtain a classification model. Then, two sub-datasets were used to train RAU-Net to get two segmentation models. Finally, the test set was entered into the network, and the network outputs were post-processed to obtain the final segmentation results. The Dice coefficient, sensitivity, specificity and intersection over union (IoU) index of the method proposed in this paper reached 0.938 8, 0.952 3, 0.998 5 and 0.876 8, respectively. It proves that the proposed method can be used to segment DCE-MRI breast tumors effectively and accurately.

Key words: fully convolutional network (FCN), image segmentation, breast tumor, dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI)

中图分类号:

邱玥, 聂生东, 魏珑. 基于全卷积网络的乳腺肿瘤动态增强磁共振图像分割[J]. 波谱学杂志, 2022, 39(2): 196-207.

Yue QIU, Sheng-dong NIE, Long WEI. Segmentation of Breast Tumors Based on Fully Convolutional Network and Dynamic Contrast Enhanced Magnetic Resonance Image[J]. Chinese Journal of Magnetic Resonance, 2022, 39(2): 196-207.

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结构		输入	输出	结构		输入	输出
block1	conv	128²*1	128²*32	block4	conv	16²*128	16²*64
	BN	128²*32	128²*32		BN	16²*64	16²*64
	max-pooling	128²*32	64²*32		max-pooling	16²*64	8²*64
block2	conv	64²*32	64²*64	block5	conv	8²*64	8²*32
	BN	64²*64	64²*64		BN	8²*32	8²*32
	max-pooling	64²*64	32²*64		max-pooling	8²*32	4²*32
block3	conv	32²*64	32²*128	flatten		4²*32	512
	BN	32²*128	32²*128	dense		512	64
	max-pooling	32²*128	16²*128	dense		64	2

结构^*	输入	输出
conv	128²*1	128²*64
conv	128²*64	128²*64
max-pooling	128²*64	64²*64
RA_block	64²* 64	64²*64
max-pooling	64²*64	32²*64
conv	32²*64	32²*128
conv	32²*128	32²*128
max-pooling	32²*128	16²*128
RA_block	16²*128	16²*128
max-pooling	16²*128	8²*128
conv	8²*256	8²*256
conv	8²*256	8²*256
up-conv	8²*256	16²*128
merge	16²128, 16²128	16²*256
RA_block	16²*256	16²*256
up-conv	16²*256	32²*128
merge	32²128, 32²128	32²*256
conv	32²*256	32²*128
conv	32²*128	32²*128
up-conv	32²*128	64²*64
merge	64²64, 64²64	64²*128
RA_block	64²*128	64²*128
up-conv	64²*128	128²*64
merge	128²64, 128²64	128²*128
conv	128²*128	128²*64
conv	128²*64	128²*2
conv	128²*2	128²*1

结构^*	输入^#	输出^#
conv	a²*c	a²*c
conv	a²*c	a²*c
GAP	a²*c	c
Dense+ReLU	c	c/4
Dense+Sigmoid	c/4	c
multiply	a²*c, c	a²*c
add	a²c, a²c	a²*c

方法		Dice系数		敏感度	特异性	IoU	耗时/s
方法		无后处理	有后处理	敏感度	特异性	IoU	耗时/s
U-Net		0.9001	0.9057	0.9469	0.9994	0.8425	1.8931
ResU-Net		0.9056	0.9142	0.9296	0.9988	0.8495	1.7236
U-Net++		0.9175	0.9198	0.9201	0.9985	0.8601	2.0370
RAU-Net		0.9202	0.9254	0.9566	0.9990	0.8624	1.7936

CBP5-Net+ RAU-Net	大肿瘤	0.9506	0.9579	0.9580	0.9983	0.9001
	小肿瘤	0.9167	0.9197	0.9466	0.9988	0.8535	1.8162
	平均	0.9336	0.9388	0.9523	0.9985	0.8768

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