Neural Radiance Projection
This work addresses segmentation challenges in medical imaging, specifically for X-ray data, but appears incremental as it builds on existing methods like GANs and UNet.
The paper tackled the problem of training convolutional neural networks for X-ray image segmentation by addressing shortages like limited annotated datasets, label ambiguity, and class imbalance, resulting in improved performance over vanilla UNet models.
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.