An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan

2025-07-11

AI Summary

To address high computational costs, heavy reliance on large-scale annotated datasets, and poor segmentation accuracy for small muscles in MRI-based muscle segmentation, this paper proposes a training-free, unsupervised framework. Our method leverages keypoint detection combined with Lucas-Kanade optical flow tracking to achieve temporal alignment of muscle structures across adjacent MRI slices and subsequent 3D reconstruction. By eliminating deep learning models, the approach significantly reduces computational overhead and annotation dependency while enhancing interpretability and cross-population generalizability. Under multi-strategy keypoint configurations, the method achieves mean Dice coefficients of 0.60–0.70—comparable to state-of-the-art CNN-based methods. This work establishes an efficient, robust, and interpretable paradigm for rapid clinical muscle quantification and MRI analysis in resource-constrained settings.

Technology Category

Computer Vision: SegmentationMachine Learning: Calibration & Uncertainty QuantificationNatural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

Web Mining and Content Analysis: Large pretrained models with web dataUser Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalizationSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Automated muscle segmentation faces high computational costs
• Existing methods lack accuracy for smaller muscles
• CNN-based approaches have limited generalizability and interpretability

Innovation

• Keypoint tracking for muscle segmentation
• Lucas-Kanade optical flow integration
• Training-free approach reduces computational cost