# SSDD-GAN: Single-Step Denoising Diffusion GAN for Cochlear Implant Surgical Scene Completion

arXiv: https://arxiv.org/abs/2502.05710 · HTML: https://mlsanity.com/paper/2502.05710/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 8 Feb 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 14 of 14)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Transformation-Grounded Image Generation Network for Novel 3D View Synthesis](https://mlsanity.com/paper/1703.02921/) (2017)
- [Painting Outside the Box: Image Outpainting with GANs](https://mlsanity.com/paper/1808.08483/) (2018)
- [Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery](https://mlsanity.com/paper/2403.07219/) (2024)
- [Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label](https://mlsanity.com/paper/2302.07967/) (2023)
- [Self-supervised Mamba-based Mastoidectomy Shape Prediction for Cochlear Implant Surgery](https://mlsanity.com/paper/2407.15787/) (2024)
- [Post-mastoidectomy Surface Multi-View Synthesis from a Single Microscopy Image](https://mlsanity.com/paper/2409.03190/) (2024)

## Built on by (most cited first, 2 of 2)

- [Vision6D: 3D-to-2D Interactive Visualization and Annotation Tool for 6D Pose Estimation](https://mlsanity.com/paper/2504.15329/) (2025)
- [Monocular Marker-free Patient-to-Image Intraoperative Registration for Cochlear Implant Surgery](https://mlsanity.com/paper/2505.18381/) (2025)

## Abstract (arXiv)

Recent deep learning-based image completion methods, including both inpainting and outpainting, have demonstrated promising results in restoring corrupted images by effectively filling various missing regions. Among these, Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPMs) have been employed as key generative image completion approaches, excelling in the field of generating high-quality restorations with reduced artifacts and improved fine details. In previous work, we developed a method aimed at synthesizing views from novel microscope positions for mastoidectomy surgeries; however, that approach did not have the ability to restore the surrounding surgical scene environment. In this paper, we propose an efficient method to complete the surgical scene of the synthetic postmastoidectomy dataset. Our approach leverages self-supervised learning on real surgical datasets to train a Single-Step Denoising Diffusion-GAN (SSDD-GAN), combining the advantages of diffusion models with the adversarial optimization of GANs for improved Structural Similarity results of 6%. The trained model is then directly applied to the synthetic postmastoidectomy dataset using a zero-shot approach, enabling the generation of realistic and complete surgical scenes without the need for explicit ground-truth labels from the synthetic postmastoidectomy dataset. This method addresses key limitations in previous work, offering a novel pathway for full surgical microscopy scene completion and enhancing the usability of the synthetic postmastoidectomy dataset in surgical preoperative planning and intraoperative navigation.