End-to-End Convolutional Neural Network Framework for Breast Ultrasound Analysis Using Multiple Parametric Images Generated from Radiofrequency Signals

Applied Sciences

Soohyun Kim¹  Joonhwan Yi¹  Juyoung Park²  Hyungsuk Kim² 

[1] Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea;Department of Electrical Engineering, Kwangwoon University, Seoul 01897, Korea;

关键词: medical ultrasound imaging;    breast ultrasound;    deep learning techniques;    convolutional neural network;    quantitative ultrasound;    B-mode image;   

DOI  :  10.3390/app12104942

来源: DOAJ

【 摘 要 】

Breast ultrasound (BUS) is an effective clinical modality for diagnosing breast abnormalities in women. Deep-learning techniques based on convolutional neural networks (CNN) have been widely used to analyze BUS images. However, the low quality of B-mode images owing to speckle noise and a lack of training datasets makes BUS analysis challenging in clinical applications. In this study, we proposed an end-to-end CNN framework for BUS analysis using multiple parametric images generated from radiofrequency (RF) signals. The entropy and phase images, which represent the microstructural and anatomical information, respectively, and the traditional B-mode images were used as parametric images in the time domain. In addition, the attenuation image, estimated from the frequency domain using RF signals, was used for the spectral features. Because one set of RF signals from one patient produced multiple images as CNN inputs, the proposed framework overcame the limitation of datasets in a broad sense of data augmentation while providing complementary information to compensate for the low quality of the B-mode images. The experimental results showed that the proposed architecture improved the classification accuracy and recall by 5.5% and 11.6%, respectively, compared with the traditional approach using only B-mode images. The proposed framework can be extended to various other parametric images in both the time and frequency domains using deep neural networks to improve its performance.

【 授权许可】

Unknown