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

arXiv: https://arxiv.org/abs/2507.08690 · HTML: https://mlsanity.com/paper/2507.08690/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation](https://mlsanity.com/paper/2203.15259/) (2022)
- [PL-CVIO: Point-Line Cooperative Visual-Inertial Odometry](https://mlsanity.com/paper/2311.05717/) (2023)
- [Visual Navigation Using Sparse Optical Flow and Time-to-Transit](https://mlsanity.com/paper/2111.09669/) (2021)
- [NeRF-VIO: Map-Based Visual-Inertial Odometry with Initialization Leveraging Neural Radiance Fields](https://mlsanity.com/paper/2503.07952/) (2025)
- [PLK-Calib: Single-shot and Target-less LiDAR-Camera Extrinsic Calibration using Pl"ucker Lines](https://mlsanity.com/paper/2503.07955/) (2025)

## Abstract (arXiv)

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.