Neural Radiance Projection

Pham Ngoc Huy; Tran Minh Quan

doi:10.1109/isbi52829.2022.9761457

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arXiv · 2203.07658

Neural Radiance Projection

Pham Ngoc Huy·Tran Minh Quan

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Abstract

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.

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eess.IV cs.CV cs.GR

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BibTeX RIS

Pham Ngoc Huy, Tran Minh Quan. 2022-03-15. Neural Radiance Projection. https://doi.org/10.1109/isbi52829.2022.9761457

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