Medical Scene Reconstruction and Segmentation based on 3D Gaussian Representation

TL;DR

This paper introduces an efficient 3D Gaussian-based reconstruction method for medical images that improves structural continuity, semantic consistency, and computational efficiency, especially under sparse data conditions.

Contribution

The proposed method combines 3D Gaussian and tri-plane representations to enhance medical image reconstruction accuracy and efficiency over traditional approaches.

Findings

High-quality, anatomically coherent reconstructions on US and MRI datasets

Significant improvements in reconstruction efficiency

Enhanced structural continuity and semantic stability under sparse slices

Abstract

3D reconstruction of medical images is a key technology in medical image analysis and clinical diagnosis, providing structural visualization support for disease assessment and surgical planning. Traditional methods are computationally expensive and prone to structural discontinuities and loss of detail in sparse slices, making it difficult to meet clinical accuracy requirements.To address these challenges, we propose an efficient 3D reconstruction method based on 3D Gaussian and tri-plane representations. This method not only maintains the advantages of Gaussian representation in efficient rendering and geometric representation but also significantly enhances structural continuity and semantic consistency under sparse slicing conditions. Experimental results on multimodal medical datasets such as US and MRI show that our proposed method can generate high-quality, anatomically coherent, and semantically stable medical images under sparse data conditions, while significantly improving reconstruction efficiency. This provides an efficient and reliable new approach for 3D visualization and clinical analysis of medical images.