论文标题
神经辐射投影
Neural Radiance Projection
论文作者
论文摘要
所提出的方法，神经辐射投影（NERP）解决了训练的三个最根本的短缺，X射线图像分割上的卷积神经网络：处理缺失/有限的人类通知数据集；每个像素标签上的模棱两可；以及正面和负类分布之间的不平衡。通过利用生成的对抗网络，我们可以合成大量基于物理的X射线图像，所谓的变异重建X光片（VRR），以及它们从更准确的标记的3D计算机层析成像数据中的分割。结果，VRR比其他投射方法更忠实地表现出照片现实的指标。添加NERP的输出还超过了在同一对X射线图像上训练的香草UNET模型。
The proposed method, Neural Radiance Projection (NeRP), addresses the three most fundamental shortages of training such a convolutional neural network on X-ray image segmentation: dealing with missing/limited human-annotated datasets; ambiguity on the per-pixel label; and the imbalance across positive- and negative- classes distribution. By harnessing a generative adversarial network, we can synthesize a massive amount of physics-based X-ray images, so-called Variationally Reconstructed Radiographs (VRRs), alongside their segmentation from more accurate labeled 3D Computed Tomography data. As a result, VRRs present more faithfully than other projection methods in terms of photo-realistic metrics. Adding outputs from NeRP also surpasses the vanilla UNet models trained on the same pairs of X-ray images.
