结合贝叶斯和深度学习方法用于超声图像中扇形的描绘
Combining Bayesian and Deep Learning Methods for the Delineation of the Fan in Ultrasound Images
摘要：
超声（US）图像通常包含超声扇区域外的识别信息和超声医师在检查期间放置的手动注释。为了在深度学习框架中利用这些图像，首先需要描绘出划分超声扇形区域的扇形边界，然后删除内部的其他注释。我们提出了一个参数概率方法的第一个任务。我们利用这种方法来生成一个训练数据集与感兴趣的区域（ROI）的分割掩模和训练一个U-网络执行相同的任务，在监督的方式，从而大大减少了该方法的计算时间，160倍的速度。然后，使用现有的修复方法处理这些图像，以移除风扇区域内存在的注释。据我们所知，这是第一个快速检测超声图像中风扇的参数方法，除了图像本身之外没有任何其他信息。
英文摘要：
Ultrasound (US) images usually contain identifying information outside the ultrasound fan area and manual annotations placed by the sonographers during exams. For those images to be exploitable in a Deep Learning framework, one needs to first delineate the border of the fan which delimits the ultrasound fan area and then remove other annotations inside. We propose a parametric probabilistic approach for the first task. We make use of this method to generate a training data set with segmentation masks of the region of interest (ROI) and train a U-Net to perform the same task in a supervised way, thus considerably reducing computational time of the method, one hundred and sixty times faster. These images are then processed with existing inpainting methods to remove annotations present inside the fan area. To the best of our knowledge, this is the first parametric approach to quickly detect the fan in an ultrasound image without any other information than the image itself.
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