User menu

Accès à distance ? S'identifier sur le proxy UCLouvain

A gradient-based method for segmenting FDG-PET images: methodology and validation.

Primary tabs

Geets, Xavier Lee, John Aldo [UCL] Bol, Anne [UCL] Lonneux, Max [UCL] Grégoire, Vincent [UCL]

PURPOSE: A new gradient-based method for segmenting FDG-PET images is described and validated. METHODS: The proposed method relies on the watershed transform and hierarchical cluster analysis. To allow a better estimation of the gradient intensity, iteratively reconstructed images were first denoised and deblurred with an edge-preserving filter and a constrained iterative deconvolution algorithm. Validation was first performed on computer-generated 3D phantoms containing spheres, then on a real cylindrical Lucite phantom containing spheres of different volumes ranging from 2.1 to 92.9 ml. Moreover, laryngeal tumours from seven patients were segmented on PET images acquired before laryngectomy by the gradient-based method and the thresholding method based on the source-to-background ratio developed by Daisne (Radiother Oncol 2003;69:247-50). For the spheres, the calculated volumes and radii were compared with the known values; for laryngeal tumours, the volumes were compared with the macroscopic specimens. Volume mismatches were also analysed. RESULTS: On computer-generated phantoms, the deconvolution algorithm decreased the mis-estimate of volumes and radii. For the Lucite phantom, the gradient-based method led to a slight underestimation of sphere volumes (by 10-20%), corresponding to negligible radius differences (0.5-1.1 mm); for laryngeal tumours, the segmented volumes by the gradient-based method agreed with those delineated on the macroscopic specimens, whereas the threshold-based method overestimated the true volume by 68% (p=0.014). Lastly, macroscopic laryngeal specimens were totally encompassed by neither the threshold-based nor the gradient-based volumes. CONCLUSION: The gradient-based segmentation method applied on denoised and deblurred images proved to be more accurate than the source-to-background ratio method.

Document type Article de périodique (Journal article) – Journal Article, Research Support, Non-U.S. Gov't
Access type Accès restreint
Publication date 2007
Journal information "European Journal of Nuclear Medicine and Molecular Imaging" - Vol. 34, no. 9, p. 1427-1438 (2007)
Peer reviewed yes
Publisher Springer ((Germany) Heidelberg)
issn 1619-7070
e-issn 1619-7089
Publication status Publié
Affiliation UCL - MD/MINT - Département de médecine interne
MESH Subject Algorithms ; Cluster Analysis ; Fluorodeoxyglucose F18 - pharmacology ; Head and Neck Neoplasms - diagnosis - pathology ; Humans ; Image Processing, Computer-Assisted ; Imaging, Three-Dimensional ; Inflammation ; Pattern Recognition, Automated ; Phantoms, Imaging ; Polymethyl Methacrylate - chemistry ; Positron-Emission Tomography - methods ; Radiopharmaceuticals - pharmacology ; Subtraction Technique
Links
  1. Mohan R, Wu Q, Manning M, Schmidt-Ullrich R. Radiobiological consideration in the design of fractionation strategies for intensity-modulated radiation therapy of the head and neck. Int J Radiat Oncol Biol Phys 2000;46:619–30.
  2. Rasch C, Eisbruch A, Remeiejer P. Irradiation of paranasal sinus tumors: a delineation and dose comparison study. Int J radiat Oncol Biol Phys 2002;52:120–7.
  3. Ciernik IF, Dizendorf E, Baumert BG, Reiner B, Burger C, Davis JB, et al. Radiation treatment planning with an integrated positron emission and computer tomography (PET/CT): a feasibility study. Int J Radiat Oncol Biol Phys 2003;57:853–63.
  4. Nishioka T, Shiga T, Shirato H, Tsukamoto E, Tsuchiya K, Kato T, et al. Image fusion between 18FDG-PET and MRI/CT for radiotherapy planning of oropharyngeal and nasopharyngeal carcinomas. Int J Radiat Oncol Biol Phys 2002;53:1051–7.
  5. Paulino AC, Koshy M, Howell R, Schuster D, Davis LW. Comparison of CT- and FDG-PET-defined gross tumor volume in intensity-modulated radiotherapy for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2005;61:1385–92.
  6. Gregoire V, Daisne JF, Geets X. Comparison of CT- and FDG-PET-defined GT: in regard to Paulino et al. Int J Radiat Oncol Biol Phys 2005;63:308–9.
  7. Daisne JF, Sibomana M, Bol A, Doumont T, Lonneux M, Gregoire V. Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms. Radiother Oncol 2003;69:247–50.
  8. Daisne JF, Gregoire V, Duprez T, Lonneux M, Hamoir M, Reychler H, et al. Tumor volume in pharyngolaryngeal squamous cell carcinoma: comparison at CT, MR imaging, and FDG PET and validation with surgical specimen. Radiology 2004;233:93–100.
  9. Elad M. On the origin of the bilateral filter and ways to improve it. IEEE Trans Image Processing 2002; 11:1141–51.
  10. Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Machine Intell 1990;12(7):629–39.
  11. King MA, Penney BC, Glick SJ. An image-dependent Metz filter for nuclear medicine images. J Nucl Med 1988;29:1980–9.
  12. Herholz K. Non-stationary spatial filtering and accelerated curve fitting for parametric imaging with dynamic PET. Eur J Nucl Med 1988;14:477–84.
  13. Carasso AS. Linear and nonlinear image deblurring: a documented study. SIAM J Numer Anal 1999;36:1659–89.
  14. van Cittert PH. Z Phys 1931;69:298.
  15. Landweber L. An iteration formula for Fredholm integral equations of the 1rst kind. Am J Math 1951;73:615–24.
  16. Lagendijk Reginald L., Biemond Jan, Iterative Identification and Restoration of Images, ISBN:9781461367789, 10.1007/978-1-4615-3980-3
  17. Beucher S. The watershed transformation applied to image segmentation. Scanning Microscopy International 1992;Suppl 6:299–314.
  18. Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Machine Intell 1991; 13(6):583–98.
  19. Mariano-Goulart D, Collet H, Kotzki P-O, Zanca M. Semi-automatic segmentation of gated blood pool emission tomographic images by watersheds: application to the determination of right and left ejection fractions. Eur J Nucl Med 1998;25:1300–7.
  20. Sijbers J, Scheunders P, Verhoye M, Van der Linden A, Van Dyck D, Raman E. Watershed-based segmentation of 3D MR data for volume quantization. Magn Reson Imaging 1997;15:679–88.
  21. Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Computer Surveys 1999;31:264–323.
  22. Hudson HM, Larkin RS. Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging 1994;13:601–9.
  23. Geets X, Daisne JF, Gregoire V, Hamoir M, Lonneux M. Role of 11-C-methionine positron emission tomography for the delineation of the tumor volume in pharyngo-laryngeal squamous cell carcinoma: comparison with FDG-PET and CT. Radiother Oncol 2004;71:267–73.
  24. Geets X, Daisne JF, Tomsej M, Duprez T, Lonneux M, Gregoire V. Impact of the type of imaging modality on target volumes delineation and dose distribution in pharyngo-laryngeal squamous cell carcinoma: comparison between pre- and per-treatment studies. Radiother Oncol 2006;78:291–7.
  25. Vauclin S, Doyeux K, Hapdey S, Vassal M, Vera P, Gardin I. Comparison of three thresholding methods for tumor volume determination in 18F-FDG PET imaging. Eur J Nucl Med 2006;33(Suppl 2):S148.
  26. Devroye Luc, Non-Uniform Random Variate Generation, ISBN:9781461386452, 10.1007/978-1-4613-8643-8
  27. Daisne JF, Sibomana M, Bol A, Cosnard G, Lonneux M, Gregoire V. Evaluation of a multimodality image (CT, MRI and PET) coregistration procedure on phantom and head and neck cancer patients: accuracy, reproducibility and consistency. Radiother Oncol 2003;69:237–45.
  28. Black QC, Grills IS, Kestin LL, Wong CY, Wong JW, Martinez AA, et al. Defining a radiotherapy target with positron emission tomography. Int J Rad Oncol Biol Phys 2004;60:1272–82.
  29. Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rube C, et al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. J Nucl Med 2005;46:1342–8.
  30. Wienhard K, Dahlbom M, Heiss WD, Michel C, Bruckbauer T, Pietrzyk U, et al. The ECAT EXACT HR: performance of a new high resolution positron scanner. J Comput Assist Tomogr 1994;18:110–8.
  31. Chen CH, Muzic RF, Nelson D, Adler LP. Simultaneous recovery of size and radioactivity concentration of small spheroids with PET data. J Nucl Med 1999;40:118–30.
  32. Gilbeau L, Octave-Prignot M, Loncol T, Renard L, Scalliet P, Gregoire V. Comparison of setup accuracy of three different thermoplastic masks for the treatment of brain and head and neck tumors. Radiother Oncol 2001;58:155–62.
  33. Hamlet Sandra, Ezzell Gary, Aref Amr, Larynx motion associated with swallowing during radiation therapy, 10.1016/0360-3016(94)90073-6
  34. Hong TS, Tome WA, Chappell RJ, Harari PM. Variations in target delineation for head and neck IMRT: an international multi-institutional study. Int J Radiat Oncol Biol Phys 2004;60:157–8.
  35. Urie M.M., Goitein M., Doppke K., Kutcher J.G., LoSasso T., Mohan R., Munzenrider J.E., Sontag M., Wong J.W., The role of uncertainty analysis in treatment planning, 10.1016/0360-3016(91)90170-9
  36. Doshi NK, Shao Y, Silverman RW, Cherry SR. Design and evaluation of an LSO PET detector for the breast cancer imaging. Med Phys 2000;27:1535–43.
  37. Leahy R, Qi J. Statistical approaches in quantitative positron emission tomography. Stat Comput 2000;10:147–65.
Bibliographic reference Geets, Xavier ; Lee, John Aldo ; Bol, Anne ; Lonneux, Max ; Grégoire, Vincent. A gradient-based method for segmenting FDG-PET images: methodology and validation.. In: European Journal of Nuclear Medicine and Molecular Imaging, Vol. 34, no. 9, p. 1427-1438 (2007)
Permanent URL http://hdl.handle.net/2078.1/10926