Liu, M., & Lee, J. (2025, June), BOARD #107: An Efficient Approach for Muscle Segmentation and 3D Modeling Using Keypoint Tracking in MRI Scans  Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . 10.18260/1-2--55924

\bibitem{asee_peer_55924}
  Liu, M., \& Lee, J. (2025, June), \emph{BOARD \#107: An Efficient Approach for Muscle Segmentation and 3D Modeling Using Keypoint Tracking in MRI Scans}
  Paper presented at 2025 ASEE Annual Conference \& Exposition , Montreal, Quebec, Canada .
  10.18260/1-2--55924

Mengyuan Liu and Jeongkyu Lee.  "BOARD #107: An Efficient Approach for Muscle Segmentation and 3D Modeling Using Keypoint Tracking in MRI Scans".  2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada , 2025, June.  ASEE Conferences, 2025.  https://peer.asee.org/55924 Internet.  23 Aug, 2025

\bibitem{asee_peer_55924}
  Mengyuan Liu and Jeongkyu Lee.
  "BOARD \#107: An Efficient Approach for Muscle Segmentation and 3D Modeling Using Keypoint Tracking in MRI Scans".
  \emph{2025 ASEE Annual Conference \& Exposition , Montreal, Quebec, Canada , 2025, June}.
  ASEE Conferences, 2025.
  https://peer.asee.org/55924 Internet.  23 Aug, 2025

@INPROCEEDINGS{asee_peer_55924
author = "Mengyuan Liu and Jeongkyu Lee"
title = "BOARD \#107: An Efficient Approach for Muscle Segmentation and 3D Modeling Using Keypoint Tracking in MRI Scans"
booktitle = "2025 ASEE Annual Conference \& Exposition "
year = "2025"
month = "June"
address = "Montreal, Quebec, Canada "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/55924}
number = {10.18260/1-2--55924}
}

TY  - CPAPER
AB  - 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.
AU  - Mengyuan Liu
AU  - Jeongkyu Lee
CY  - Montreal, Quebec, Canada 
DA  - 2025/06/22
PB  - ASEE Conferences
TI  - BOARD #107: An Efficient Approach for Muscle Segmentation and 3D Modeling Using Keypoint Tracking in MRI Scans
UR  - https://peer.asee.org/55924
DO  - 10.18260/1-2--55924
ER  -