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

2025-02-08

AI Summary

The scarcity of complete surgical scenes in synthetic mastoidectomy datasets hinders preoperative planning and intraoperative navigation. Method: This paper proposes a zero-shot scene completion method tailored to otologic microscope scenarios. We introduce a novel single-step denoising diffusion GAN architecture that synergistically integrates the structural prior modeling capability of Denoising Diffusion Probabilistic Models (DDPMs) with the adversarial generation strength of Generative Adversarial Networks (GANs). Furthermore, we establish a new paradigm comprising self-supervised pretraining on real surgical videos followed by zero-shot transfer to synthetic data—requiring no ground-truth annotations. Contribution/Results: Our method achieves a 6% SSIM improvement over baseline approaches, significantly enhancing spatial coherence and clinical realism of synthetic scenes, thereby providing high-fidelity visual support for virtual surgical planning and real-time navigation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Completing cochlear implant surgical scenes
• Enhancing surgical scene reconstruction accuracy
• Applying zero-shot learning for scene generation

Innovation

• Single-Step Denoising Diffusion-GAN
• Self-supervised learning on real datasets
• Zero-shot approach for scene completion