论文标题
目标解剖结构的分离和胸部X光片的遮挡
Separation of target anatomical structure and occlusions in chest radiographs
论文作者
论文摘要
胸部X光片通常是进行筛查和诊断的低成本检查。但是，X光片是3D结构的2D表示，导致杂乱无章阻碍视觉检查和自动图像分析。在这里，我们提出了一个完全卷积的网络，以抑制X光片的特定任务，同时保留相关图像信息（例如肺pare骨）。所提出的算法从高分辨率CT扫描中创建了重建的X光片和地面真相数据。结果表明，消除与分类任务无关的视觉变化可改善只有有限的训练数据时分类器的性能。这尤其重要，因为在医学成像中，较少的地面实际情况很常见。
Chest radiographs are commonly performed low-cost exams for screening and diagnosis. However, radiographs are 2D representations of 3D structures causing considerable clutter impeding visual inspection and automated image analysis. Here, we propose a Fully Convolutional Network to suppress, for a specific task, undesired visual structure from radiographs while retaining the relevant image information such as lung-parenchyma. The proposed algorithm creates reconstructed radiographs and ground-truth data from high resolution CT-scans. Results show that removing visual variation that is irrelevant for a classification task improves the performance of a classifier when only limited training data are available. This is particularly relevant because a low number of ground-truth cases is common in medical imaging.
