Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows
Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows
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DOI：
10.3390/app11031127
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发表时间：
2021-01
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影响因子：
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通讯作者：
S. Yasutomi;T. Arakaki;R. Matsuoka;Akira Sakai;R. Komatsu;K. Shozu;A. Dozen;Hidenori Machino
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作者：
S. Yasutomi;T. Arakaki;R. Matsuoka;Akira Sakai;R. Komatsu;K. Shozu;A. Dozen;Hidenori Machino
声影是医学超声成像中常见的伪影。阴影是由反射超声的物体（如骨骼）引起的，它们在超声图像中显示为暗区。检测这种阴影对于评估图像质量至关重要。这将是一个预处理，为进一步的图像处理或识别，旨在计算机辅助诊断。在本文中，我们提出了一个自动编码结构，估计阴影区域及其强度。该模型通过其编码器和解码器将输入图像一次分割为估计阴影图像和估计无阴影图像。然后，它将它们组合起来以重建输入。通过基于超声图像上相对粗糙的特定领域知识生成合理的合成阴影，我们可以使用未标记的数据来训练模型。如果阴影的像素级标签可用，我们也以半监督的方式利用它们。通过对用于胎儿心脏诊断的超声图像的实验，我们表明，我们的方法在DICE评分中达到0.720，优于传统的图像处理方法和基于深度神经网络的分割方法。实验结果也表明了该方法在估计阴影强度和无阴影图像方面的能力。
Acoustic shadows are common artifacts in medical ultrasound imaging. The shadows are caused by objects that reflect ultrasound such as bones, and they are shown as dark areas in ultrasound images. Detecting such shadows is crucial for assessing the quality of images. This will be a pre-processing for further image processing or recognition aiming computer-aided diagnosis. In this paper, we propose an auto-encoding structure that estimates the shadowed areas and their intensities. The model once splits an input image into an estimated shadow image and an estimated shadow-free image through its encoder and decoder. Then, it combines them to reconstruct the input. By generating plausible synthetic shadows based on relatively coarse domain-specific knowledge on ultrasound images, we can train the model using unlabeled data. If pixel-level labels of the shadows are available, we also utilize them in a semi-supervised fashion. By experiments on ultrasound images for fetal heart diagnosis, we show that our method achieved 0.720 in the DICE score and outperformed conventional image processing methods and a segmentation method based on deep neural networks. The capability of the proposed method on estimating the intensities of shadows and the shadow-free images is also indicated through the experiments.