Concordia University, 1493 Saint-Catherine St W, Montreal, Quebec, Canada.
Nuance Communications, 1500 Boulevard Robert-Bourassa, Montreal, Quebec, H3A 3S7, Canada.
Int J Comput Assist Radiol Surg. 2020 Jun;15(6):981-988. doi: 10.1007/s11548-020-02158-3. Epub 2020 Apr 29.
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.
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.
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%.
The proposed two-stage technique shows promising results for segmentation of breast US images and has a much smaller chance of failure.
使用超声成像检测乳房病变是计算机辅助诊断系统的一个重要应用。已经提出了几种用于乳房病变检测和分割的自动方法；然而，由于超声伪影以及病变形状和位置的复杂性，从超声乳房图像中分割病变或肿瘤仍然是一个开放的问题。在本文中，我们提出在分割阶段之前使用病变检测阶段，以提高分割的准确性。
我们使用了一个包含 163 张乳房图像的乳房超声成像数据集，其中包括良性病变或恶性肿瘤。首先，我们使用 U-Net 来检测病变，然后使用另一个 U-Net 来分割检测到的区域。我们可以证明，当病变被精确检测到时，分割性能会显著提高；然而，如果检测阶段不够精确，分割阶段也会失败。因此，我们开发了一种测试时增强技术来评估检测阶段的性能。
通过使用所提出的两阶段方法，我们可以整体提高平均 Dice 评分 1.8%。对于原始 Dice 评分低于 70%的图像，改进更为显著，平均 Dice 评分提高了 14.5%。
所提出的两阶段技术为乳房超声图像的分割展示了有前景的结果，并且失败的可能性要小得多。
