In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region of interest (ROI) as an input for computerised breast ultrasound image analysis. This task is time consuming and there is inconsistency among human experts. Researchers attempting to automate the process of obtaining the ROIs have been relying on image processing and conventional machine learning methods. We propose the use of a deep learning method for breast ultrasound ROI detection and lesion localisation. We use the most accurate object detection deep learning framework – Faster-RCNN with Inception-ResNet-v2 – as our deep learning network. Due to the lack of datasets, we use transfer learning and propose a new 3-channel artificial RGB method to improve the overall performance. We evaluate and compare the performance of our proposed methods on two datasets (namely, Dataset A and Dataset B), i.e. within individual datasets and composite dataset. We report the lesion detection results with two types of analysis: (1) detected point (centre of the segmented region or the detected bounding box) and (2) Intersection over Union (IoU). Our results demonstrate that the proposed methods achieved comparable results on detected point but with notable improvement on IoU. In addition, our proposed 3-channel artificial RGB method improves the recall of Dataset A. Finally, we outline some future directions for the research.

在当前的乳腺超声计算机辅助诊断系统中，放射科医师预先选择感兴趣区域 (ROI) 作为计算机化乳腺超声图像分析的输入。这项任务非常耗时，并且人类专家之间存在不一致。试图使获得 ROI 的过程自动化的研究人员一直依赖于图像处理和传统的机器学习方法。我们建议使用深度学习方法进行乳房超声 ROI 检测和病变定位。我们使用最准确的对象检测深度学习框架——Faster-RCNN 和 Inception-ResNet-v2——作为我们的深度学习网络。由于缺乏数据集，我们使用迁移学习并提出了一种新的 3 通道人工 RGB 方法来提高整体性能。我们评估和比较我们提出的方法在两个数据集（即数据集 A 和数据集 B）上的性能，即在单个数据集和复合数据集内。我们使用两种类型的分析报告病变检测结果：（1）检测到的点（分割区域的中心或检测到的边界框）和（2）联合上的交集（IoU）。我们的结果表明，所提出的方法在检测点上取得了可比的结果，但在IoU上有显着的改进。此外，我们提出的 3 通道人工 RGB 方法提高了数据集 A的召回率。最后，我们概述了一些未来的研究方向。