Consistency and Comparison of Medical Image Registration-Segmentation and Mathematical Model for Glioblastoma Volume Progression

Abstract

Tumor volume progression and calculation is a very common task in cancer research and image processing. Tumor volume analysis can be carried out in two ways. The first way is using different mathematical formulas and the second way is using image registration-segmentation method. In this paper an objective application of registration of multiple brain imaging scans with segmentation is used to investigate brain tumor growth in a 3 dimensional (3D) manner. Using 3D medical image registration-segmentation algorithm, multiple scans of MR images of a patient who has brain tumor are registered with different MR images of the same patient acquired at a different time so that growth of the tumor inside the patient's brain can be investigated. Brain tumor volume measurement is also achieved using mathematical model based formulas in this paper. Medical image registration-segmentation and mathematical based method are implemented to 19 patients and satisfactory results are obtained. An advantageous point of medical image registration-segmentation method for brain tumor investigation is that grown, diminished, and unchanged brain tumor parts of the patients are investigated and computed on an individual basis in a three-dimensional (3D) manner within the time. This paper is intended to provide a comprehensive reference source for researchers involved in medical image registration, segmentation and tumor growth investigation.

Keywords

• Brain tumor growth
• Medical image segmentation
• Medical image registration
• Tumor volume computing

References

1. Lisa M. DeAngelis, Brain tumors, Med. Prog. N Engl J Med. 114 (2001) 114–123. doi:10.1056/NEJM200101113440207.
2. B.A. Kohler, E. Ward, B.J. McCarthy, M.J. Schymura, L.A.G. Ries, C. Eheman, A. Jemal, R.N. Anderson, U.A. Ajani, B.K. Edwards, Annual report to the nation on the status of cancer, 1975–2007, featuring tumors of the brain and other nervous system, J. Natl. Cancer Inst. (2011).
3. G. Mazzara, R. Velthuizen, J. Pearlman, H. Greenberg, H. Wagner, Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation., Int J Radiat Oncol Biol Phys. 59 (2004) 300–312. doi:10.1016/j.ijrobp.2004.01.026.
4. F.K.H. van Landeghem, K. Maier-Hauff, A. Jordan, K.T. Hoffmann, U. Gneveckow, R. Scholz, B. Thiesen, W. Brück, A. von Deimling, Post-mortem studies in glioblastoma patients treated with thermotherapy using magnetic nanoparticles, Biomaterials. 30 (2009) 52–57. doi:10.1016/j.biomaterials.2008.09.044.
5. D. Krex, B. Klink, C. Hartmann, A. Von Deimling, T. Pietsch, M. Simon, M. Sabel, J.P. Steinbach, O. Heese, G. Reifenberger, M. Weller, G. Schackert, Long-term survival with glioblastoma multiforme, Brain. 130 (2007) 2596–2606. doi:10.1093/brain/awm204.
6. R.B. Seither, B. Jose, K.J. Paris, R.D. Lindberg, W.J. Spanos, Results of irradiation in patients with high-grade gliomas evaluated by magnetic resonance imaging., Am. J. Clin. Oncol. 18 (1995) 297–299.
7. JM. Caudrelier, S. Vial, D. Gibon, C. Kulik, C. Fournier, B. Castelain, B. Coche-Dequeant, J. Rousseau, MRI definition of target volumes using fuzzy logic method for three-dimensional conformal radiation therapy, Int. J. Radiat. Oncol. Biol. Phys. 55 (2003) 225–233.
8. R.K. Ten Haken, A.F. Thornton, H.M. Sandler, M.L. LaVigne, D.J. Quint, B.A. Fraass, M.L. Kessler, D.L. McShan, A quantitative assessment of the addition of MRI to CT-based, 3-D treatment planning of brain tumors, Radiother. Oncol. 25 (1992) 121–133.

9. E.C. Halperin, G. Bentel, E.R. Heinz, P.C. Burger, Radiation therapy treatment planning in supratentorial glioblastoma multiforme: an analysis based on post mortem topographic anatomy with CT correlations, Int. J. Radiat. Oncol. Biol. Phys. 17 (1989) 1347–1350.
10. S.W. Lee, B.A. Fraass, L.H. Marsh, K. Herbort, S.S. Gebarski, M.K. Martel, E.H. Radany, A.S. Lichter, H.M. Sandler, Patterns of failure following high-dose 3-D conformal radiotherapy for high-grade astrocytomas: a quantitative dosimetric study, Int. J. Radiat. Oncol. Biol. Phys. 43 (1999) 79–88.
11. V.S. Khoo, E.J. Adams, F. Saran, J.L. Bedford, J.R. Perks, A.P. Warrington, M. Brada, A comparison of clinical target volumes determined by CT and MRI for the radiotherapy planning of base of skull meningiomas, Int. J. Radiat. Oncol. Biol. Phys. 46 (2000) 1309–1317.
12. P. Sminia, R. Mayer, External beam radiotherapy of recurrent glioma: radiation tolerance of the human brain, Cancers (Basel). 4 (2012) 379–399.
13. R.K. Ten Haken, B.A. Fraass, A.S. Lichter, L.H. Marsh, E.H. Radany, H.M. Sandler, A brain tumor dose escalation protocol based on effective dose equivalence to prior experience, Int. J. Radiat. Oncol. Biol. Phys. 42 (1998) 137–141.
14. D.H. Char, S. Kroll, T.L. Phillips, Uveal melanoma: growth rate and prognosis, Arch. Ophthalmol. 115 (1997) 1014–1018.
15. J.M. Romero, P.T. Finger, R.B. Rosen, R. Iezzi, Three-dimensional ultrasound for the measurement of choroidal melanomas, Arch. Ophthalmol. 119 (2001) 1275–1282.
16. T. Grasbon, S. Schriever, J.P. Hoops, A.J. Mueller, 3D-Ultraschall Erste Erfahrungen bei verschiedenen Augenerkrankungen, Der Ophthalmol. 98 (2001) 88–93.
17. W. Li, E.S. Gragoudas, K.M. Egan, Tumor basal area and metastatic death after proton beam irradiation for choroidal melanoma, Arch. Ophthalmol. 121 (2003) 68–72.
18. E. Richtig, G. Langmann, K. Müllner, G. Richtig, J. Smolle, Calculated tumour volume as a prognostic parameter for survival in choroidal melanomas., Eye (Lond). 18 (2004) 619–623. doi:10.1038/sj.eye.6701806.
19. Y. Liu, S.M. Sadowski, A.B. Weisbrod, E. Kebebew, R.M. Summers, J. Yao, Patient specific tumor growth prediction using multimodal images, Med. Image Anal. 18 (2014) 555–566. doi:10.1016/j.media.2014.02.005.
20. R. Guthoff, Modellmessungen zur Volumenbestimmung des malignen Aderhautmelanoms, Graefe’s Arch. Clin. Exp. Ophthalmol. 214 (1980) 139–146.
21. H. Rubin, P. Arnstein, B.M. Chu, Tumor progression in nude mice and its representation in cell culture, J. Natl. Cancer Inst. 77 (1986) 1125–1135.
22. H. Rubin, B.M. Chu, P. Arnstein, Selection and adaptation for rapid growth in culture of cells from delayed sarcomas in nude mice, Cancer Res. 47 (1987) 486–492.
23. S. Karpagam, S. Gowri, Brain Tumor Growth and Volume Detection by Ellipsoid-Diameter Technique Using MRI Data, Int. J. Comput. Sci. 9 (2012) 121–126.
24. M.F. Dempsey, B.R. Condon, D.M. Hadley, Measurement of tumor “size” in recurrent malignant glioma: 1D, 2D, or 3D?, AJNR Am. J. Neuroradiol. 26 (2005) 770–776.
25. A. Talkington, R. Durrett, Estimating tumor growth rates in vivo, V (2014) 1–27. doi:10.1007/s11538-015-0110-8.Estimating.
26. PALA, Tuba , CAMURCU, Ali Yilmaz . "Design of Decision Support System in the Metastatic Colorectal Cancer Data Set and Its Application". Balkan Journal of Electrical and Computer Engineering 4 / 1 (Mart 2016): 12-16).
27. NOĞAY, H. Selçuk , AKINCI, Tahir Cetin . "A Convolutional Neural Network Application for Predicting the Locating of Squamous Cell Carcinoma in the Lung". Balkan Journal of Electrical and Computer Engineering 6 / 3 (Temmuz 2018): 207-210 . https://doi.org/10.17694/bajece.455132.
28. M. Chen, A. Carass, A. Jog, J. Lee, S. Roy, J.L. Prince, Cross contrast multi-channel image registration using image synthesis for MR brain images, Med. Image Anal. 36 (2016) 2–14. doi:10.1016/j.media.2016.10.005.
29. S.E.A. Muenzing, B. van Ginneken, K. Murphy, J.P.W. Pluim, Supervised quality assessment of medical image registration: Application to intra-patient CT lung registration, Med. Image Anal. 16 (2012) 1521–1531. doi:10.1016/j.media.2012.06.010.
30. K.K. Brock, L.A. Dawson, M.B. Sharpe, D.J. Moseley, D.A. Jaffray, Feasibility of a novel deformable image registration technique to facilitate classification, targeting, and monitoring of tumor and normal tissue, Int. J. Radiat. Oncol. Biol. Phys. 64 (2006) 1245–1254.
31. M.R. Kaus, S.K. Warfield, A. Nabavi, P.M. Black, F.A. Jolesz, R. Kikinis, Automated segmentation of mr images of brain tumors 1, Radiology. 218 (2001) 586–591.
32. J.-P. Thirion, Image matching as a diffusion process: an analogy with Maxwell’s demons, Med. Image Anal. 2 (1998) 243–260.
33. I. Bloch, O. Colliot, O. Camara, T. Géraud, Fusion of spatial relationships for guiding recognition, example of brain structure recognition in 3D MRI, Pattern Recognit. Lett. 26 (2005) 449–457.
34. B. Alfano, M. Ciampi, G. De Pietro, A wavelet-based algorithm for multimodal medical image fusion, in: Int. Conf. Semant. Digit. Media Technol., Springer, 2007: pp. 117–120.
35. K. Yuanyuan, L. Bin, T. Lianfang, M. Zongyuan, Multi-modal medical image fusion based on wavelet transform and texture measure, in: Control Conf. 2007. CCC 2007. Chinese, IEEE, 2007: pp. 697–700.
36. Q.P. Zhang, M. Liang, W.C. Sun, Medical diagnostic image fusion based on feature mapping wavelet neural networks, in: Image Graph. (ICIG’04), Third Int. Conf., IEEE, 2004: pp. 51–54.
37. Q.P. Zhang, W.J. Tang, L.L. Lai, W.C. Sun, K.P. Wong, Medical diagnostic image data fusion based on wavelet transformation and self-organising features mapping neural networks, in: Mach. Learn. Cybern. 2004. Proc. 2004 Int. Conf., IEEE, 2004: pp. 2708–2712.
38. G. Quellec, M. Lamard, G. Cazuguel, B. Cochener, C. Roux, Wavelet optimization for content-based image retrieval in medical databases, Med. Image Anal. 14 (2010) 227–241. doi:10.1016/j.media.2009.11.004.
39. M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.M. Jodoin, H. Larochelle, Brain tumor segmentation with Deep Neural Networks, Med. Image Anal. 35 (2017) 18–31. doi:10.1016/j.media.2016.05.004.
40. K.M. Pohl, E. Konukoglu, S. Novellas, N. Ayache, A. Fedorov, I.F. Talos, A. Golby, W.M. Wells, R. Kikinis, P.M. Black, A new metric for detecting change in slowly evolving brain tumors: Validation in meningioma patients, Neurosurgery. 68 (2011) 225–233. doi:10.1227/NEU.0b013e31820783d5.
41. S. Bauer, R. Wiest, L.-P. Nolte, M. Reyes, A survey of MRI-based medical image analysis for brain tumor studies, Phys. Med. Biol. 58 (2013) R97–R129. doi:10.1088/0031-9155/58/13/R97.
42. E.D. Angelini, J. Delon, A.B. Bah, L. Capelle, E. Mandonnet, Differential MRI analysis for quantification of low grade glioma growth, Med. Image Anal. 16 (2012) 114–126.
43. K.F. Schmidt, M. Ziu, N.O. Schmidt, P. Vaghasia, T.G. Cargioli, S. Doshi, M.S. Albert, P.M. Black, R.S. Carroll, Y. Sun, Volume reconstruction techniques improve the correlation between histological and in vivo tumor volume measurements in mouse models of human gliomas, J. Neurooncol. 68 (2004) 207–215.
44. J.P. Feldman, R. Goldwasser, a Mathematical Model for Tumor Volume Evaluation Using Two-Dimensions, Jpurnal Appl. Quant. Methods. 4 (2009) 455–462.
45. M.M. Tomayko, C.P. Reynolds, Determination of subcutaneous tumor size in athymic (nude) mice, Cancer Chemother. Pharmacol. 24 (1989) 148–154. doi:10.1007/BF00300234.
46. X. Du, J. Dang, Y. Wang, S. Wang, T. Lei, A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images, Comput. Math. Methods Med. 2016 (2016). doi:10.1155/2016/7419307.
47. P.J. Baldevbhai, R.S. Anand, Color Image Segmentation for Medical Images using L * a * b * Color Space, J. Electron. Commun. Eng. 1 (2012) 24–45. doi:10.9790/2834-0122445.
48. V.S. Rathore, M.S. Kumar, A. Verma, Colour Based Image Segmentation Using L * A * B * Colour Space Based On Genetic Algorithm, Int. J. Emerg. Technol. Adv. Eng. 2 (2012) 156–162.
49. D. Barboriak, Data From RIDER_NEURO_MRI., Cancer Imaging Arch. http//doi.org/10.7937/K9/TCIA.2015.VOSN3HN1. (2015).
50. K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, F. Prior, The Cancer Imaging Archive ( TCIA ): Maintaining and Operating a Public Information Repository, (2013) 1045–1057. doi:10.1007/s10278-013-9622-7.