International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

Refinement of Ground Truth Data for X-ray Coronary Artery Angiography (CAG) using Active Contour Model

  • Dongjin Han (Department of Fire Safety, Kyung-Il university) ;
  • Youngjoon Park (Department of Radiological Technology, Cheju-Halla University)
  • Received : 2023.10.12
  • Accepted : 2023.10.23
  • Published : 2023.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

We present a novel method aimed at refining ground truth data through regularization and modification, particularly applicable when working with the original ground truth set. Enhancing the performance of deep neural networks is achieved by applying regularization techniques to the existing ground truth data. In many machine learning tasks requiring pixel-level segmentation sets, accurately delineating objects is vital. However, it proves challenging for thin and elongated objects such as blood vessels in X-ray coronary angiography, often resulting in inconsistent generation of ground truth data. This method involves an analysis of the quality of training set pairs - comprising images and ground truth data - to automatically regulate and modify the boundaries of ground truth segmentation. Employing the active contour model and a recursive ground truth generation approach results in stable and precisely defined boundary contours. Following the regularization and adjustment of the ground truth set, there is a substantial improvement in the performance of deep neural networks.

Keywords

References

  1. Frangi, A.F.; Niessen, W.J.; Vincken, K.L.; Viergever, M.A. Multiscale vessel enhancement filtering. International conference on medical image computing and computer-assisted intervention. pp. 130-137, Springer, 1998. 
  2. Hernandez-Vela, A.; Gatta, C.; Escalera, S.; Igual, L.; Martin-Yuste, V.; Sabate, M.; Radeva, P. Accurate coronary centerline extraction, caliber estimation, and catheter detection in angiographies. IEEE Transactions on Information Technology in Biomedicine 16, pp. 1332-1340, 2012.  https://doi.org/10.1109/TITB.2012.2220781
  3. Schaap, M.; Metz, C.T.; van Walsum, T.; van der Giessen, A.G.; Weustink, A.C.; Mollet, N.R.; Bauer, C.; Bogunovic', H.; Castro, C.; Deng, X.; others. Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Medical image analysis, 13, 701-714, 2009.  https://doi.org/10.1016/j.media.2009.06.003
  4. Nasr-Esfahani, E.; Samavi, S.; Karimi, N.; Soroushmehr, S.R.; Ward, K.; Jafari, M.H.; Felfeliyan, B.; Nallamothu, B.; Najarian, K. Vessel extraction in X-ray angiograms using deep learning. 2016 38th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, 643-646, 2016. 
  5. Shin, S.Y.; Lee, S.; Yun, I.D.; Lee, K.M. Deep vessel segmentation by learning graphical connectivity. Medical image analysis, 58, 101556, 2019.
  6. Yang, S.; Kweon, J.; Roh, J.H.; Lee, J.H.; Kang, H.; Park, L.J.; Kim, D.J.; Yang, H.; Hur, J.; Kang, D.Y.; Lee, P.H.; Ahn, J.M.; Kang, S.J.; Park, D.W.; Lee, S.W.; Kim, Y.H.; Lee, C.W.; Park, S.W.; Park, S.J. Deep learning segmentation of major vessels in X-ray coronary angiography. Scientific Reports, 9, 16897, 2019. doi:10.1038/s41598-019-53254-7. 
  7. Rennie, C.; Shome, R.; Bekris, K.E.; De Souza, A.F. A dataset for improved rgbd-based object detection and pose estimation for warehouse pick-and-place. IEEE Robotics and Automation Letters, 1, 1179-1185, 2016.  https://doi.org/10.1109/LRA.2016.2532924
  8. Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, 234-241, 2015. 
  9. Kass, M.; Witkin, A.; Terzopoulos, D. Snakes: Active contour models. International journal of computer vision, 1, 321-331, 1988.  https://doi.org/10.1007/BF00133570
  10. Caselles, V.; Kimmel, R.; Sapiro, G. Geodesic active contours. International journal of computer vision, 22, 61-79, 1997.  https://doi.org/10.1023/A:1007979827043
  11. Chan, T.F.; Vese, L.A. Active contours without edges. IEEE Transactions on image processing, 10, 266-277, 2001.  https://doi.org/10.1109/83.902291