In recent years, automated whole breast ultrasound (ABUS) has drawn attention to breast disease detection and diagnosis applications. However, reviewing ABUS volumes is a time-costing task and some subtle tumors may be missed. In this paper, a 3D multi-view tumor detection method is proposed for ABUS volumes. Firstly, a layer connected feature extraction network is designed for Faster R-CNN. Then, orthogonal multi-view slices are reconstructed and detected using this modified Faster R-CNN to extract 2D candidates. Finally, a 3D multi-view position analysis scheme is designed to fuse 2D detection results and get final 3D bounding boxes. The performance of this proposed method is evaluated on a data set of 158 volumes from 75 patients by 5-fold cross-validation. Experimental results show that our method achieves a sensitivity of 95.06% with 0.57 false positives (FPs) per volume. Compared with existing detection methods, the proposed method is more effective and general.

近年来，自动全乳超声（ABUS）已引起人们对乳腺疾病检测和诊断应用的关注。但是，检查ABUS量是一项耗时的工作，并且可能会漏掉一些细微的肿瘤。本文提出了一种针对ABUS体积的3D多视图肿瘤检测方法。首先，为Faster R-CNN设计了一个层连接的特征提取网络。然后，使用此修改后的Faster R-CNN重建并检测正交多视图切片，以提取2D候选对象。最后，设计了一种3D多视图位置分析方案，以融合2D检测结果并获得最终的3D边界框。通过5倍交叉验证，在来自75位患者的158卷数据集上评估了此提议方法的性能。实验结果表明，我们的方法在0时达到了95.06％的灵敏度。每卷57个误报（FP）。与现有的检测方法相比，该方法更有效，更通用。