PURPOSE Detecting breast lesions using ultrasound imaging is an important application of computer-aided diagnosis systems. Several automatic methods have been proposed for breast lesion detection and segmentation; however, due to the ultrasound artefacts, and to the complexity of lesion shapes and locations, lesion or tumor segmentation from ultrasound breast images is still an open problem. In this paper, we propose using a lesion detection stage prior to the segmentation stage in order to improve the accuracy of the segmentation. METHODS We used a breast ultrasound imaging dataset which contained 163 images of the breast with either benign lesions or malignant tumors. First, we used a U-Net to detect the lesions and then used another U-Net to segment the detected region. We could show when the lesion is precisely detected, the segmentation performance substantially improves; however, if the detection stage is not precise enough, the segmentation stage also fails. Therefore, we developed a test-time augmentation technique to assess the detection stage performance. RESULTS By using the proposed two-stage approach, we could improve the average Dice score by 1.8% overall. The improvement was substantially more for images wherein the original Dice score was less than 70%, where average Dice score was improved by 14.5%. CONCLUSIONS The proposed two-stage technique shows promising results for segmentation of breast US images and has a much smaller chance of failure.

中文翻译：

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使用U-Net的两阶段超声图像分割和测试时间增强。

目的利用超声成像检测乳腺病变是计算机辅助诊断系统的重要应用。已经提出了几种用于乳腺病变检测和分割的自动方法。然而，由于超声伪像以及病变形状和位置的复杂性，超声乳腺图像对病变或肿瘤的分割仍然是一个未解决的问题。在本文中，我们建议在分割阶段之前使用病变检测阶段，以提高分割的准确性。方法我们使用了乳房超声成像数据集，其中包含163例乳腺良性病变或恶性肿瘤的图像。首先，我们使用U-Net检测病变，然后使用另一个U-Net分割检测到的区域。我们可以显示何时精确检测到病变，分割效果大大提高；但是，如果检测阶段不够精确，则分割阶段也会失败。因此，我们开发了一种测试时间增强技术来评估检测阶段的性能。结果通过使用建议的两阶段方法，我们可以将Dice的平均得分总体提高1.8％。对于原始Dice得分小于70％的图像，平均Dice得分提高了14.5％，图像的改善幅度更大。结论所提出的两阶段技术显示出有希望的乳腺US图像分割结果，并且失败的可能性要小得多。我们开发了一种测试时间增强技术来评估检测阶段的性能。结果通过使用建议的两阶段方法，我们可以将Dice的平均得分总体提高1.8％。对于原始Dice得分小于70％的图像，平均Dice得分提高了14.5％，图像的改善幅度更大。结论所提出的两阶段技术显示出有希望的乳腺US图像分割结果，并且失败的可能性要小得多。我们开发了一种测试时间增强技术来评估检测阶段的性能。结果通过使用建议的两阶段方法，我们可以将Dice的平均得分总体提高1.8％。对于原始Dice得分小于70％的图像，平均Dice得分提高了14.5％，图像的改善幅度更大。结论所提出的两阶段技术显示出有希望的乳腺US图像分割结果，并且失败的可能性要小得多。