一种双视图信息融合的乳腺肿块自动检测算法  

An Automatic Breast Mass Detection Algorithm with Dual-view Information Fusion

作　　者：蒋慧琴[1,2] 王博霖 马岭 于湛[3] 徐红卫[4] JIANG Huiqin;WANG Bolin;MA Ling;YU Zhan;XU Hongwei(School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China;Digital Medical Image Research Center, Zhengzhou Universiyt, Zhengzhou 450001, China;Department of Radiology, the First Affiliated Hospital of Zhengzhou University,Zhengzhou 450052, China;Department of Radiology, the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052,China)

机构地区：[1]郑州大学信息工程学院,河南郑州450001 [2]郑州大学数字化影像技术研究中心,河南郑州450001 [3]郑州大学第一附属医院放射科,河南郑州450052 [4]郑州大学第五附属医院影像科,河南郑州450052

出　　处：《郑州大学学报（理学版）》2020年第4期28-36,共9页Journal of Zhengzhou University:Natural Science Edition

基　　金：河南省联合基金重点项目(U1604262)。

摘　　要：针对基于单视图的深度学习乳腺肿块检测算法假阳性率较高问题,提出一种双视图信息融合的乳腺肿块自动检测算法。首先建立双曲正割模型,利用互相关法自动搜索乳腺X线摄影图像中的肿块感兴趣区域,并根据胸壁线、乳头位置在双侧头尾位和内外侧斜位图像上建立物理坐标系,筛选标注假阳性区域以在数据层扩充监督学习信息;其次,设计空间金字塔池化模块有效融合基于YOLOv3主干网络提取的多尺度局部特征以提高检测敏感性;最后,在类别损失函数中增加聚焦参数,通过调节算法学习过程以提高检测特异性。充分利用双视图数据提供的先验信息提高检测正确率,实验结果表明检测敏感性达到92.0%,特异性达到87.7%,平均每幅图像假阳性0.041个,其检测性能较原模型大幅提升,且具有较好的鲁棒性。Aiming to reduce the high false positive rate of breast mass detection algorithm based on single view deep learning,an automatic breast mass detection algorithm with dual-view information fusion was proposed.Firstly,Sech model was established and the cross-correlation method was used to automatically search the region of interest in breast X-ray images.According to the chest wall line and nipple position,a physical coordinate system was established on bilateral craniocaudal and mediolateral oblique images to screen and label the false positive areas to expand the supervised learning information in the data layer.Furthermore,the spatial pyramid pooling(SPP)was designed to effectively fuse the multi-scale local features extracted from the you only look once V3(YOLOV3)backbone network to improve the detection sensitivity.Finally,focusing parameters were added to the category loss function to improve the specificity of detection by adjusting the algorithm learning process.The proposed method made full use of the prior information provided by the dual-view data to improve the detection accuracy,the method achieved a sensitivity value of 92.0%,and specificity value of 87.7%,with 0.041 FP/I.Compared with the original model,the detection performance of the proposed model was greatly improved and has good robustness.

关 键 词：乳腺癌 乳腺X线摄影 计算机辅助诊断 双视图 YOLOv3 faster-RCNN 空间金字塔池化 聚焦损失函数 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]