Neural Radiance Projection
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 15 Mar 2022.
Builds on
7 of 19 references.
References · 7
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
- 2016Improved Techniques for Training GANscited by 2,946
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,287
- 2018Demystifying MMD GANscited by 1,277
- 2020A U-Net Based Discriminator for Generative Adversarial Networkscited by 128
- 2018DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedurescited by 55
- 2020XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priorscited by 8
Built on by
Citing papers per year: 2023: 2.
2 papers.
- 2023Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs18 Oct 2023
- 2023Denoising Diffusion Medical Models19 Apr 2023