Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey

Boyu Zhang; Yingtao Zhang; H. D. Cheng; Min Xian; Shan Gai; Olivia Cheng; Kuan Huang

Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey

Computer Vision and Pattern Recognition 2021-02-17 v1

Authors: Boyu Zhang , Yingtao Zhang , H. D. Cheng , Min Xian , Shan Gai , Olivia Cheng , Kuan Huang

Abstract

Osteoarthritis (OA) is one of the major health issues among the elderly population. MRI is the most popular technology to observe and evaluate the progress of OA course. However, the extreme labor cost of MRI analysis makes the process inefficient and expensive. Also, due to human error and subjective nature, the inter- and intra-observer variability is rather high. Computer-aided knee MRI segmentation is currently an active research field because it can alleviate doctors and radiologists from the time consuming and tedious job, and improve the diagnosis performance which has immense potential for both clinic and scientific research. In the past decades, researchers have investigated automatic/semi-automatic knee MRI segmentation methods extensively. However, to the best of our knowledge, there is no comprehensive survey paper in this field yet. In this survey paper, we classify the existing methods by their principles and discuss the current research status and point out the future research trend in-depth.

Keywords

medical diagnosis

Cite

@article{arxiv.1802.04894,
  title  = {Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey},
  author = {Boyu Zhang and Yingtao Zhang and H. D. Cheng and Min Xian and Shan Gai and Olivia Cheng and Kuan Huang},
  journal= {arXiv preprint arXiv:1802.04894},
  year   = {2021}
}

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10 pages, 6 tables

Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey

Abstract

Keywords

Cite

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