基于多模态MRI与深度学习的乳腺病变良恶性鉴别

doi:10.11938/cjmr20222969

摘要/Abstract

摘要：

为提高基于动态增强磁共振成像（DCE-MRI）的计算机辅助（CAD）方法对乳腺病变良恶性鉴别的精度，本文基于多模态特征融合，提出一种联合非对称卷积和超轻子空间注意模块的卷积神经网络AC_Ulsam_CNN.首先，采用迁移学习方法预训练模型，筛选出对乳腺病变良恶性鉴别最为有效的DCE-MRI扫描时序.而后，基于最优扫描时序图像，搭建基于AC_Ulsam_CNN网络的模型，以增强分类模型的特征表达能力和鲁棒性.最后，将影像特征与乳腺影像数据报告和数据系统（BI-RADS）分级、表观扩散系数（ADC）和时间-信号强度曲线（TIC）类型等多模态信息进行特征融合，以进一步提高模型对病灶的预测性能.采用五折交叉验证方法进行模型验证，本文方法获得了0.826的准确率（ACC）和0.877的受试者工作曲线下面积（AUC）.这表明该算法在小样本量数据下可较好区分乳腺病变的良恶性，而基于多模态数据的融合模型也进一步丰富了特征信息，从而提高病灶的检出精度，为乳腺病灶良恶性的自动鉴别诊断提供了新方法.

关键词: 动态增强磁共振成像, 卷积神经网络, 多模态特征融合, 乳腺病变, 良恶性鉴别

Abstract:

To improve the accuracy of computer aided diagnosis (CAD) based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in the differentiation of benign and malignant breast lesions, this study proposed a convolutional neural network model (AC_Ulsam_CNN) that is based on multi-modal feature fusion and the combination of asymmetric convolution (AC) and ultra-lightweight subspace attention module (Ulsam). Firstly, the transfer learning method was used to pre-train the model to screen out the most effective DCE-MRI time phase scan for differentiating benign and malignant breast lesions. Then, a network model based on AC_Ulsam_CNN was constructed based on the optimal time phase scan images to enhance the feature expression ability and robustness of the classification model. Finally, multimodal information such as breast imaging reporting and data system (BI-RADS) classification, apparent diffusion coefficient (ADC) and time-signal intensity curve (TIC) type were incorporated for feature fusion, to further improve the distinguishing performance of benign and malignant breast lesions. The performance of the model was verified by 5-fold cross-validation method, and the accuracy (ACC) of the proposed method was 0.826 and the area under the curve (AUC) was 0.877. The experimental results show that the proposed algorithm performs well in the classification of benign and malignant breast lesions with small sample size, and the fusion model based on multimodal data further enriches the feature information, thus this study improves the detection accuracy of lesions, and provides a new method for automatic differential diagnosis of benign and malignant breast lesions.

Key words: dynamic contrast enhanced magnetic resonance imaging, convolutional neural network, multimodal feature fusion, breast lesions, differentiation of benign and malignant lesions

中图分类号:

TP394.1

杨一风, 祁章璇, 聂生东. 基于多模态MRI与深度学习的乳腺病变良恶性鉴别[J]. 波谱学杂志, 2022, 39(4): 401-412.

Yi-feng YANG, Zhang-xuan QI, Sheng-dong NIE. Differentiation of Benign and Malignant Breast Lesions Based on Multimodal MRI and Deep Learning[J]. Chinese Journal of Magnetic Resonance, 2022, 39(4): 401-412.

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参考文献 24

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良性病变		恶性病变
病变表现	病例数	病变表现	病例数
纤维腺瘤	48	粘液癌	9
乳腺病	43	导管原位癌	21
导管内乳头状瘤	16	浸润性导管癌	28
导管扩张	6	乳头状癌	9
导管上皮增生	6	浸润性癌	91
累计	119	累计	158

时序类别	时序相位名称
单一时序	DynS0、DynS1、DynS2、DynS3、DynS4、SdynS0、SdynS1、SdynS2、SdynS3
组合时序	DynS012、DynS013、DynS014、DynS023、DynS024、DynS034、DynS123、DynS124、 DynS134、DynS234、SdynS012、SdynS013、SdynS123

#	输入层	输入尺寸	卷积核尺寸	通道数	步长	输出尺寸
（1）	Conv1+BN+LeakyReLU	150 × 150 × 3	3 × 3	16	1	150 × 150 × 16
（2）	Conv2+BN+LeakyReLU	150 × 150 × 16	1 × 1	8	1	150 × 150 × 8
（3）	Conv3+BN+LeakyReLU	150 × 150 × 8	3 × 3	8	1	150 × 150 × 8
（4）	Conv4+BN+LeakyReLU	150 × 150 × 8	1 × 1	16	1	150 × 150 × 16
（5）	Conv5+BN+LeakyReLU	150 × 150 × 16	1 × 1	16	1	150 × 150 × 16
（6）	Add1	150 × 150 × 16	-	-	-	150 × 150 × 16
（7）	Pool1	150 × 150 × 16	3 × 3	-	3	50 × 50 × 16
（8）	Conv6	50 × 50 × 16	1 × 1	8	1	50 × 50 × 8
（9）	AC Block1	50 × 50 × 8	-	8	-	50 × 50 × 16
（10）	Conv7	50 × 50 × 16	1 × 1	32	1	50 × 50 × 32
（11）	Conv8	50 × 50 × 16	1 × 1	32	1	50 × 50 × 32
（12）	Add2	50 × 50 × 32	-	-	-	50 × 50 × 32
（13）	Pool2	50 × 50 × 32	3 × 3	-	3	16 × 16 × 32
（14）	Conv9	16 × 16 × 32	1 × 1	32	1	16 × 16 × 32
（15）	AC Block2	16 × 16 × 32	-	32	-	16 × 16 × 64
（16）	Conv10	16 × 16 × 64	1 × 1	128	1	16 × 16 × 128
（17）	Conv11	50 × 50 × 32	3 × 3	16	3	16 × 16 × 16
（18）	Conv12	16 × 16 × 32	1 × 1	128	1	16 × 16 × 128
（19）	Add3	16 × 16 × 128	-	-	-	16 × 16 × 128
（20）	Concatenate1	16 × 16 × 128 16 × 16 × 16	-	-	-	16 × 16 × 144
（21）	Ulsam Block1	16 × 16 × 144	-	-	-	16 × 16 × 144
（22）	Ulsam Block2	16 × 16 × 144	-	-	-	16 × 16 × 144
（23）	Concatenate2	16 × 16 × 144 16 × 16 × 144	-	-	-	16 × 16 × 288
（24）	Conv13+BN+LeakyReLU	16 × 16 × 288	1 × 1	128	1	16 × 16 × 128
-	GAP	16 × 16 × 128	-	-	-	4096
-	Dense	4096	-	-	-	16
-	Sigmoid	16	-	-	-	1

网络模型结构	时序	ACC	AUC	Sens	Spec
InceptionV3	DynS4	0.725	0.730	0.763	0.620
InceptionV3	DynS024	0.749	0.775	0.789	0.664
AC_Ulsam_CNN	DynS4	0.771	0.784	0.733	0.790
AC_Ulsam_CNN	DynS024	0.762	0.826	0.783	0.758

特征类型	ACC	AUC	Sens	Spec
DynS024	0.762	0.826	0.783	0.758
DynS024+BI-RADS	0.769	0.840	0.791	0.754
DynS024+ADC	0.807	0.863	0.822	0.792
DynS024+TIC	0.778	0.829	0.810	0.746
DynS024+BI-RADS+ADC+TIC	0.826	0.877	0.861	0.783