Nowadays, breast cancer is the leading cause of cancer death for women all over the world. Since the reason of breast cancer is unknown, early detection of the disease plays an important role in cancer control, saving lives and reducing costs. Among different modalities, automated 3-D breast ultrasound (3-D ABUS) is a new and effective imaging modality which has attracted a lot of interest as an adjunct to mammography for women with dense breasts. However, reading ABUS images is time consuming for radiologists and subtle abnormalities may be overlooked. Hence, computer aided detection (CADe) systems can be utilized as a second interpreter to assist radiologists to increase their screening speed and sensitivity. In this paper, a general architecture representing different CADe systems for ABUS images is introduced and the approaches for implementation of each block are categorized and reviewed. In addition, the limitations of these systems are discussed and their performance in terms of sensitivity and number of false positives per volume are compared.

中文翻译：

---

自动 3-D 乳腺超声图像中的计算机辅助检测：一项调查

如今，乳腺癌是全世界女性癌症死亡的主要原因。由于乳腺癌的病因尚不清楚，因此早期发现该疾病对于癌症控制、挽救生命和降低成本具有重要作用。在不同的方式中，自动 3-D 乳房超声 (3-D ABUS) 是一种新的有效成像方式，作为乳房致密女性乳房 X 光检查的辅助手段引起了很多人的兴趣。然而，阅读 ABUS 图像对于放射科医生来说非常耗时，并且可能会忽略细微的异常。因此，计算机辅助检测 (CADe) 系统可用作第二个解释器，以帮助放射科医生提高筛查速度和灵敏度。在本文中，介绍了代表 ABUS 图像不同 CADe 系统的通用架构，并对每个块的实现方法进行了分类和审查。此外，还讨论了这些系统的局限性，并比较了它们在灵敏度和每卷误报数量方面的性能。