用关键点追踪实现高效肌肉分割与三维重建，无需训练且更可解释。
An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan
- 基于关键点跟踪与光流法，不依赖深度学习训练。
- 平均骰子系数达0.6~0.7，接近顶尖CNN模型性能。
- 适合临床和研究场景，计算量小、结果易解释。
磁共振成像（MRI）能非侵入性地实现高分辨率肌肉结构分析。然而，自动化分割仍受限于高计算成本、对大规模训练数据的依赖，以及在小型肌肉分割中的准确率下降。基于卷积神经网络（CNN）的方法虽强大，但常伴随显著计算开销、泛化能力有限及跨人群可解释性差的问题。本研究提出一种无需训练的分割方法，结合关键点选择与Lucas-Kanade光流算法。该方法在不同关键点选择策略下，平均骰子相似系数（DSC）达到0.6~0.7，性能媲美当前最优的CNN模型，同时大幅降低计算需求并提升可解释性。该可扩展框架为临床与科研应用提供了稳健且可解释的肌肉分割方案。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI) enables non-invasive, high-resolution analysis of muscle structures. However, automated segmentation remains limited by high computational costs, reliance on large training datasets, and reduced accuracy in segmenting smaller muscles. Convolutional neural network (CNN)-based methods, while powerful, often suffer from substantial computational overhead, limited generalizability, and poor interpretability across diverse populations. This study proposes a training-free segmentation approach based on keypoint tracking, which integrates keypoint selection with Lucas-Kanade optical flow. The proposed method achieves a mean Dice similarity coefficient (DSC) ranging from 0.6 to 0.7, depending on the keypoint selection strategy, performing comparably to state-of-the-art CNN-based models while substantially reducing computational demands and enhancing interpretability. This scalable framework presents a robust and explainable alternative for muscle segmentation in clinical and research applications.
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