An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan
This provides a scalable and explainable alternative for muscle segmentation in clinical and research applications, though it is incremental as it builds on existing keypoint and optical flow techniques.
The paper tackled the problem of automated muscle segmentation in MRI scans, which is limited by high computational costs and accuracy issues, by proposing a training-free approach using keypoint tracking; it achieved a mean Dice similarity coefficient of 0.6 to 0.7, comparable to state-of-the-art CNN models while reducing computational demands.
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.