Abstract：
OBJECTIVE: Early detection of breast cancer has a significant effect on reducing its mortality rate. For this purpose, automated three-dimensional breast ultrasound (3-D ABUS) has been recently used alongside mammography. The 3-D volume produced in this imaging system includes many slices. The radiologist must review all the slices to find the mass, a time-consuming task with a high probability of mistakes. Therefore, many computer-aided detection (CADe) systems have been developed to assist radiologists in this task. In this paper, we propose a novel CADe system for mass detection in 3-D ABUS images.
METHODS: The proposed system includes two cascaded convolutional neural networks. The goal of the first network is to achieve the highest possible sensitivity, and the second network\'s goal is to reduce false positives while maintaining high sensitivity. In both networks, an improved version of 3-D U-Net architecture is utilized in which two types of modified Inception modules are used in the encoder section. In the second network, new attention units are also added to the skip connections that receive the results of the first network as saliency maps.
RESULTS: The system was evaluated on a dataset containing 60 3-D ABUS volumes from 43 patients and 55 masses. A sensitivity of 91.48% and a mean false positive of 8.85 per patient were achieved.
CONCLUSIONS: The suggested mass detection system is fully automatic without any user interaction. The results indicate that the sensitivity and the mean FP per patient of the CADe system outperform competing techniques.
METHODS: The proposed system includes two cascaded convolutional neural networks. The goal of the first network is to achieve the highest possible sensitivity, and the second network\'s goal is to reduce false positives while maintaining high sensitivity. In both networks, an improved version of 3-D U-Net architecture is utilized in which two types of modified Inception modules are used in the encoder section. In the second network, new attention units are also added to the skip connections that receive the results of the first network as saliency maps.
RESULTS: The system was evaluated on a dataset containing 60 3-D ABUS volumes from 43 patients and 55 masses. A sensitivity of 91.48% and a mean false positive of 8.85 per patient were achieved.
CONCLUSIONS: The suggested mass detection system is fully automatic without any user interaction. The results indicate that the sensitivity and the mean FP per patient of the CADe system outperform competing techniques.
摘要：
目的：早期发现乳腺癌对降低其死亡率具有显着作用。为此,自动三维乳腺超声（3-DABUS）最近已与乳房X线照相术一起使用。在该成像系统中产生的3-D体积包括许多切片。放射科医生必须检查所有切片才能找到肿块，一个耗时的任务，错误的可能性很高。因此,许多计算机辅助检测（CADe）系统已经开发出来，以协助放射科医师完成这项任务。在本文中,我们提出了一种新颖的CADe系统，用于3-DABUS图像中的质量检测。
方法：所提出的系统包括两个级联的卷积神经网络。第一个网络的目标是实现尽可能高的灵敏度，第二个网络的目标是在保持高灵敏度的同时减少误报。在这两个网络中，使用了3-DU-Net架构的改进版本，其中编码器部分使用了两种类型的修改的Inception模块。在第二个网络中，新的关注单元也被添加到接收第一网络的结果作为显著性图的跳过连接。
结果：在包含来自43名患者和55个肿块的60个3-DABUS体积的数据集上评估了该系统。每个患者的灵敏度为91.48％，平均假阳性为8.85。
结论：建议的质量检测系统是全自动的，无需任何用户交互。结果表明，CADe系统的灵敏度和每位患者的平均FP优于竞争技术。
方法：所提出的系统包括两个级联的卷积神经网络。第一个网络的目标是实现尽可能高的灵敏度，第二个网络的目标是在保持高灵敏度的同时减少误报。在这两个网络中，使用了3-DU-Net架构的改进版本，其中编码器部分使用了两种类型的修改的Inception模块。在第二个网络中，新的关注单元也被添加到接收第一网络的结果作为显著性图的跳过连接。
结果：在包含来自43名患者和55个肿块的60个3-DABUS体积的数据集上评估了该系统。每个患者的灵敏度为91.48％，平均假阳性为8.85。
结论：建议的质量检测系统是全自动的，无需任何用户交互。结果表明，CADe系统的灵敏度和每位患者的平均FP优于竞争技术。
