手术机器人用的多模态图像融合新方法，测试时动态调参提升细节保留
TTTFusion: A Test-Time Training-Based Strategy for Multimodal Medical Image Fusion in Surgical Robots
- 测试时训练动态调整参数，实时优化融合效果
- 相比传统方法，细节提取与边缘保持能力显著提升
- 适合需要高精度实时图像处理的外科手术系统
随着手术机器人在临床中的广泛应用，提升其处理多模态医学影像的能力成为关键研究挑战。尽管传统医学图像融合方法在提高融合精度方面取得进展，但在实时性能、细粒度特征提取和边缘保留方面仍面临显著挑战。本文提出TTTFusion，一种基于测试时训练（Test-Time Training, TTT）的图像融合策略，在推理阶段动态调整模型参数，以高效融合多模态医学图像。通过在测试阶段适应输入图像数据，该方法基于输入信息优化参数，从而提升融合结果的准确性和细节保留能力。实验结果表明，与传统融合方法相比，TTTFusion在细粒度特征提取和边缘保留方面显著提升了多模态图像的融合质量。该方法不仅提高了图像融合精度，还为手术机器人的实时图像处理提供了新的技术解决方案。
原文摘要 · Abstract (English)
With the increasing use of surgical robots in clinical practice, enhancing their ability to process multimodal medical images has become a key research challenge. Although traditional medical image fusion methods have made progress in improving fusion accuracy, they still face significant challenges in real-time performance, fine-grained feature extraction, and edge preservation.In this paper, we introduce TTTFusion, a Test-Time Training (TTT)-based image fusion strategy that dynamically adjusts model parameters during inference to efficiently fuse multimodal medical images. By adapting the model during the test phase, our method optimizes the parameters based on the input image data, leading to improved accuracy and better detail preservation in the fusion results.Experimental results demonstrate that TTTFusion significantly enhances the fusion quality of multimodal images compared to traditional fusion methods, particularly in fine-grained feature extraction and edge preservation. This approach not only improves image fusion accuracy but also offers a novel technical solution for real-time image processing in surgical robots.
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