Chiao Jui-Ying, Chen Kuan-Yung, Liao Ken Ying-Kai, Hsieh Po-Hsin, Zhang Geoffrey, Huang Tzung-Chi
Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung.
Department of Radiology, Chang Bing Show Chwan Memorial Hospital, Changhua.
Medicine (Baltimore). 2019 May;98(19):e15200. doi: 10.1097/MD.0000000000015200.
Breast cancer is one of the most harmful diseases for women with the highest morbidity. An efficient way to decrease its mortality is to diagnose cancer earlier by screening. Clinically, the best approach of screening for Asian women is ultrasound images combined with biopsies. However, biopsy is invasive and it gets incomprehensive information of the lesion. The aim of this study is to build a model for automatic detection, segmentation, and classification of breast lesions with ultrasound images. Based on deep learning, a technique using Mask regions with convolutional neural network was developed for lesion detection and differentiation between benign and malignant. The mean average precision was 0.75 for the detection and segmentation. The overall accuracy of benign/malignant classification was 85%. The proposed method provides a comprehensive and noninvasive way to detect and classify breast lesions.
乳腺癌是对女性危害最大、发病率最高的疾病之一。降低其死亡率的有效方法是通过筛查尽早诊断癌症。临床上，亚洲女性最佳的筛查方法是超声图像结合活检。然而，活检具有侵入性，且获取的病变信息不全面。本研究的目的是建立一个利用超声图像对乳腺病变进行自动检测、分割和分类的模型。基于深度学习，开发了一种使用带卷积神经网络的掩码区域的技术用于病变检测以及良性和恶性的区分。检测和分割的平均精度为0.75。良性/恶性分类的总体准确率为85%。所提出的方法提供了一种全面且非侵入性的乳腺病变检测和分类方法。
