Memory-Efficient 3D High-Resolution Medical Image Synthesis Using CRF-Guided GANs

2025-03-13

AI Summary

To address the challenge of generating high-resolution, structurally consistent 3D medical images with limited GPU memory—where existing 3D GANs suffer from resolution constraints and spatial incoherence—this paper proposes an end-to-end CRF-guided multi-stage GAN architecture. Our method innovatively embeds a conditional random field (CRF) module directly into the generator to explicitly model long-range voxel-wise dependencies, thereby enforcing spatial consistency at intermediate feature levels. Furthermore, we adopt a staged generation strategy coupled with compact latent representations to substantially reduce GPU memory consumption and computational complexity during training. Evaluated on Lung CT and Brain MRI datasets, our approach surpasses state-of-the-art methods: it eliminates patch-based stitching artifacts, significantly improves anatomical continuity and global structural coherence, and enables end-to-end synthesis of full-volume, high-resolution 3D medical images.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Generative Adversarial Networks (GANs) have many potential medical imaging applications. Due to the limited memory of Graphical Processing Units (GPUs), most current 3D GAN models are trained on low-resolution medical images, these models cannot scale to high-resolution or are susceptible to patchy artifacts. In this work, we propose an end-to-end novel GAN architecture that uses Conditional Random field (CRF) to model dependencies so that it can generate consistent 3D medical Images without exploiting memory. To achieve this purpose, the generator is divided into two parts during training, the first part produces an intermediate representation and CRF is applied to this intermediate representation to capture correlations. The second part of the generator produces a random sub-volume of image using a subset of the intermediate representation. This structure has two advantages: first, the correlations are modeled by using the features that the generator is trying to optimize. Second, the generator can generate full high-resolution images during inference. Experiments on Lung CTs and Brain MRIs show that our architecture outperforms state-of-the-art while it has lower memory usage and less complexity.

Problem

• Memory-efficient 3D high-resolution medical image synthesis.
• Reducing GPU memory usage in 3D GAN models.
• Eliminating patchy artifacts in high-resolution medical images.

Innovation

• CRF-guided GANs for 3D medical image synthesis
• Memory-efficient high-resolution image generation
• Two-part generator with intermediate CRF modeling