A Comparative Study on Tissue Classification of Brain MR Images Using DIPY, SPM, and FSL Frameworks

Document Type : Original Paper

Authors

Fintech in Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran Medical physics department, Faculty of medicine, Iran university of medical sciences, Tehran, Iran

Abstract

Introduction: Complexity metrics have been suggested to characterize treatment plans based on machine parameters such as multileaf collimator (MLC) position. Several complexity metrics have been proposed and related to the Intensity-modulated radiation therapy (IMRT) quality assurance results. This study aims to evaluate aperture-based complexity metrics on MLC openings used in clinicaland establish a correlation between plan complexity and the gamma passing rate (GPR) for the IMRT plans.
Material and Methods: We implemented the aperture-based complexity metric on MLC openings of the IMRT treatment plan for breast  and central nervous system (CNS) cases . The modulation complexity score (MCS), the edge area metric (EAM), the converted area metric (CAM), the circumference/area (CPA), and the ratio monitor unit MU/Gy are evaluated in this study. The complexity score was calculated using Matlab. The MatriXX Evolution was used for dose verification. The dose distribution was  analyzed using the OmniPro-I'mRT program  and the gamma index was assessed using two criteria: 3%/3 mm and 3%/2 mm. The correlation between the calculated complexity score and the GPR  is analyzed using SPSS.
Results: The complexity score calculated by MCS, EAM, CAM, CPA, and MU/Gy shows breast plan is more complex than the CNS plan. The results of the correlation test of the complexity metric and GPR show that only the EAM metric shows a good correlation with GPR for both cases.
Conclusion: EAM strongly correlates with the gamma pass rate. The MCS, CAM, CPA, and MU/Gy have a weak correlation with the GPR.

Keywords

Main Subjects

References

  1. Miller KD, Ostrom QT, Kruchko C, Patil N, Tihan T, Cioffi G, et al. Brain and other central nervous system tumor statistics, 2021. CA: a cancer journal for clinicians. 2021 Sep;71(5):381-406. doi:10.3322/caac.21693
  2. Lin D, Wang M, Chen Y, Gong J, Chen L, Shi X, et al. Trends in intracranial glioma incidence and mortality in the United States, 1975-2018. Frontiers in oncology. 2021 Nov 1;11:748061. doi:10.3389/fonc.2021.748061
  3. Razavi SE, Khodadadi H. Segmentation of Magnetic Resonance Brain Imaging Based on Graph Theory. Iranian Journal of Medical Physics. 2020 Jan 1;17(1):48-57. doi:10.22038/ijmp.2019.35600.1447
  4. Despotović I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. Computational and mathematical methods in medicine. 2015;2015. doi:10.1155/2015/450341
  5. Duda RO. PE hart and DG Stork, pattern classification. Address: Wiley-Interscience. 2001.
  6. Warfield SK, Kaus M, Jolesz FA, Kikinis R. Adaptive, template moderated, spatially varying statistical classification. Medical image analysis. 2000 Mar 1;4(1):43-55. doi:10.1016/S1361-8415(00)00003-7
  7. Cocosco CA, Zijdenbos AP, Evans AC. A fully automatic and robust brain MRI tissue classification method. Medical image analysis. 2003 Dec 1;7(4):513-27. doi:10.1016/S1361-8415(03)00037-9
  8. Wells WM, Grimson WE, Kikinis R, Jolesz FA. Adaptive segmentation of MRI data. IEEE transactions on medical imaging. 1996 Aug;15(4):429-42. doi:10.1109/42.511747
  9. Ashburner J, Friston KJ. Unified segmentation. neuroimage. 2005 Jul 1;26(3):839-51. doi:10.1016/j.neuroimage.2005.02.018
  10. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE transactions on medical imaging. 2001 Jan;20(1):45-57. doi:10.1109/42.906424
  11. Garyfallidis E, Brett M, Amirbekian B, Rokem A, Van Der Walt S, Descoteaux M, et al. Dipy, a library for the analysis of diffusion MRI data. Frontiers in neuroinformatics. 2014 Feb 21;8:8. doi:10.3389/fninf.2014.00008
  12. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17(3):143-155. doi:10.1002/hbm.10062
  13. Collins CM, Liu W, Schreiber W, Yang QX, Smith MB. Central brightening due to constructive interference with, without, and despite dielectric resonance. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine. 2005 Feb;21(2):192-6. doi:10.1002/jmri.20245
  14. Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE transactions on medical imaging. 1998 Feb;17(1):87-97. doi:10.1109/42.668698
  15. Shattuck DW, Leahy RM. BrainSuite: an automated cortical surface identification tool. Medical image analysis. 2002 Jun 1;6(2):129-42. doi:10.1016/S1361-8415(02)00054-3
  16. Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM. Magnetic resonance image tissue classification using a partial volume model. NeuroImage. 2001 May 1;13(5):856-76. doi:10.1006/nimg.2000.0730
  17. Theodoridis S. Machine learning. Chapter 7—Classification: a tour of the classics. doi:10.1016/b978-0-12-818803-3.00016-7
  18. Li SZ. Markov random field modeling in image analysis. Springer Science & Business Media; 2009 Apr 3. doi:10.1007/978-4-431-66933-3
  19. Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annual review of biomedical engineering. 2000 Aug;2(1):315-37. doi:10.1146/annurev.bioeng.2.1.315
  20. Hunter JD. Matplotlib: A 2D graphics environment. Comput Sci Eng. 2007;9(3):90-95. doi:10.1109/MCSE.2007.55
  21. Harris CR, Millman KJ, van der Walt SJ, et al. Array programming with NumPy. Nature. 2020;585(7825):357-362. doi:10.1038/s41586-020-2649-2
  22. Zou KH, Warfield SK, Bharatha A, Tempany CM, Kaus MR, Haker SJ, Wells III WM, Jolesz FA, Kikinis R. Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Academic radiology. 2004 Feb 1;11(2):178-89. doi:10.1016/S1076-6332(03)00671-8
  23. Yao AD, Cheng DL, Pan I, Kitamura F. Deep learning in neuroradiology: a systematic review of current algorithms and approaches for the new wave of imaging technology. Radiology: Artificial Intelligence. 2020 Mar 4;2(2):e190026. doi:10.1148/ryai.2020190026
  24. Fleiss JL, Levin B, Paik MC (2003) The measurement of interrater agreement. In: Shewart WA, Wilks SS (eds). Statistical methods for rates and proportions 2003. John Wiley & Sons, p 598– doi:10.1002/0471445428.ch18
  25. Crum WR, Camara O, Hill DL. Generalized overlap measures for evaluation and validation in medical image analysis. IEEE transactions on medical imaging. 2006 Oct 30;25(11):1451-61. doi:10.1109/TMI.2006.880587
  26. DeCarli C, Maisog J, Murphy DG, Teichberg D, Rapoport SI, Horwitz B. Method for quantification of brain, ventricular, and subarachnoid CSF volumes from MR images. Journal of computer assisted tomography. 1992 Mar 1;16(2):274-84. doi:10.1097/00004728-199203000-00018
  27. Gilmore RL, Childress MD, Leonard C, Quisling R, Roper S, Eisenschenk S, et al. Hippocampal volumetrics differentiate patients with temporal lobe epilepsy and extratemporal lobe epilepsy. Archives of neurology. 1995 Aug 1;52(8):819-24. doi:10.1001/archneur.1995.00540320103017
  28. Wadhwa A, Bhardwaj A, Verma VS. A review on brain tumor segmentation of MRI images. Magnetic resonance imaging. 2019 Sep 1;61:247-59. doi:10.1016/j.mri.2019.05.043
  29. Dubey RB, Hanmandlu M, Gupta SK, Gupta SK. The brain MR image segmentation techniques and use of diagnostic packages. Academic Radiology. 2010 May 1;17(5):658-71. doi:10.1016/j.acra.2009.12.017
  30. Zamanpour SA, Ganji Z, Bigham B, Zemorshidi F, Zare H. Evaluation and Comparison of Automatic Brain Segmentation Methods Based On the Gold Standard Method. Iranian Journal of Medical Physics/Majallah-I Fīzīk-I Pizishkī-i Īrān. 2023 Jul 1;20(4). doi:10.22038/ijmp.2022.66025.2134

 

 

 

 

 

Iranian Journal of Medical Physics
Volume 21, Issue 2
March and April 2024
Pages 78-83

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History

  • Receive Date: 16 November 2022
  • Revise Date: 04 March 2023
  • Accept Date: 05 March 2023

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