← 研踪 PaperTrailarXiv:2509.16806 · v5
MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging
MedGS：面向多模态三维医学成像的高斯泼溅
首次提交 2025-09-21 04:52
版本更新 2026-11-13 02:27
雷达观测 2026-11-13 14:15
摘要
Multi-modal three-dimensional (3D) medical imaging data, derived from ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT), provide a widely adopted approach for non-invasive anatomical visualization. However, accurate modeling depends on surface reconstruction and frame-to-frame interpolation, where traditional methods often struggle with image noise and incomplete information between sparse frames. To address these challenges, we present \our{}, a novel framework based on Gaussian Splatting (GS) designed for high-fidelity 3D anatomical reconstruction. Uniquely, \our{} employs a multi-task architecture that simultaneously performs frame interpolation and segmentation using a unified geometric representation. By coupling these tasks, the model leverages dense signals from image synthesis to regularize the geometry, enabling high-quality surface extraction even from a limited number of input frames. Specifically, medical data are modeled as Folded-Gaussians with dual color attributes, supported by an In-Between Frame Regularization (IBFR) mechanism. Experimental results demonstrate that \our{} achieves higher metric scores than implicit neural representations and improves interpolation quality. Repository: https://github.com/gmum/MedGS
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v5 · 2026-11-13 02:27 · 最新观测版本
MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging
Multi-modal three-dimensional (3D) medical imaging data, derived from ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT), provide a widely adopted approach for non-invasive anatomical visualization. However, accurate modeling depends on surface reconstruction and frame-to-frame interpolation, where traditional methods often struggle with image noise and incomplete information between sparse frames. To address these challenges, we present \our{}, a novel framework based on Gaussian Splatting (GS) designed for high-fidelity 3D anatomical reconstruction. Uniquely, \our{} employs a multi-task architecture that simultaneously performs frame interpolation and segmentation using a unified geometric representation. By coupling these tasks, the model leverages dense signals from image synthesis to regularize the geometry, enabling high-quality surface extraction even from a limited number of input frames. Specifically, medical data are modeled as Folded-Gaussians with dual color attributes, supported by an In-Between Frame Regularization (IBFR) mechanism. Experimental results demonstrate that \our{} achieves higher metric scores than implicit neural representations and improves interpolation quality. Repository: https://github.com/gmum/MedGS
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