乳腺癌的早期发现对于降低其死亡率具有显着效果。为此，自动化三维乳腺超声 (3-D ABUS) 最近已与乳房 X 线摄影一起使用。该成像系统中产生的 3D 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，这是一项耗时的任务，而且出错的可能性很高。因此，人们开发了许多计算机辅助检测（CADe）系统来协助放射科医生完成这项任务。在本文中，我们提出了一种新颖的 CADe 系统，用于 3-D ABUS 图像中的质量检测。该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，都采用了 3-D U-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后的 Inception 模块。在第二个网络中，新的注意力单元也被添加到跳跃连接中，这些连接接收第一个网络的结果作为显着图。该系统在包含来自 43 名患者和 55 个肿块的 60 个 3-D ABUS 体积的数据集上进行了评估。灵敏度为 91.48%，每位患者的平均假阳性率为 8.85。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 系统的灵敏度和每个患者的平均 FP 优于竞争技术。版权所有 © 2024 Associazione Italiana di Fisica Medica e Sanitaria。由爱思唯尔有限公司出版。保留所有权利。

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.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.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.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.Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.