Abstract
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.
Abstract (translated)
磁共振成像（MRI）能够进行非侵入性的高分辨率肌肉结构分析。然而，自动分割仍然受限于高昂的计算成本、对大规模训练数据集的依赖以及在较小肌肉分割时准确度降低的问题。基于卷积神经网络（CNN）的方法虽然强大，但常常面临显著的计算开销、泛化能力有限和解释性较差等问题，尤其是在面对多样化的人群时。 本研究提出了一种基于关键点跟踪的无需训练的分割方法，该方法结合了关键点选择与Lucas-Kanade光流算法。所提出的这种方法在不同关键点选择策略下可以达到0.6到0.7之间的平均Dice相似系数（DSC），其表现可媲美最先进的CNN基模型，同时大大降低了计算需求并增强了解释性。 该可扩展框架为临床和研究应用中的肌肉分割提供了一个稳健且易于理解的替代方案。
URL
https://arxiv.org/abs/2507.08690
