Two-stage ultrasound image segmentation using U-Net and test time augmentation
Two-stage ultrasound image segmentation using U-Net and test time augmentation
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DOI：
10.1007/s11548-020-02158-3
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发表时间：
2020-04-29
影响因子：
3
通讯作者：
Rivaz, Hassan
中科院分区：
文献类型：
--
作者：
Amiri, Mina;Brooks, Rupert;Rivaz, Hassan
目的利用超声图像检测乳腺病变是计算机辅助诊断系统的重要应用。目前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，由于超声伪影的存在，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悬而未决的问题。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。方法使用乳腺超声成像数据集，其中包含163张乳腺良性病变和恶性肿瘤的图像。首先，我们使用U-Net检测病变，然后使用另一个U-Net对检测区域进行分割。我们可以看到，当病灶被精确检测到时，分割性能大大提高；但是，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间增加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，我们可以将Dice的平均得分提高1.8%。对于原始Dice分数低于70%的图像，改进的效果更大，其中平均Dice分数提高了14.5%。结论采用两阶段分割技术对乳腺超声图像进行分割具有较好的效果，且分割失败的可能性较小。
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.