Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
권호 표지
DOI QR코드

DOI QR Code

Optimization of Multi-Atlas Segmentation with Joint Label Fusion Algorithm for Automatic Segmentation in Prostate MR Imaging

  • Choi, Yoon Ho (Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University) ;
  • Kim, Jae-Hun (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Kim, Chan Kyo (Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine)
  • Received : 2020.04.02
  • Accepted : 2020.07.18
  • Published : 2020.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: Joint label fusion (JLF) is a popular multi-atlas-based segmentation algorithm, which compensates for dependent errors that may exist between atlases. However, in order to get good segmentation results, it is very important to set the several free parameters of the algorithm to optimal values. In this study, we first investigate the feasibility of a JLF algorithm for prostate segmentation in MR images, and then suggest the optimal set of parameters for the automatic prostate segmentation by validating the results of each parameter combination. Materials and Methods: We acquired T2-weighted prostate MR images from 20 normal heathy volunteers and did a series of cross validations for every set of parameters of JLF. In each case, the atlases were rigidly registered for the target image. Then, we calculated their voting weights for label fusion from each combination of JLF's parameters (rpxy, rpz, rsxy, rsz, β). We evaluated the segmentation performances by five validation metrics of the Prostate MR Image Segmentation challenge. Results: As the number of voxels participating in the voting weight calculation and the number of referenced atlases is increased, the overall segmentation performance is gradually improved. The JLF algorithm showed the best results for dice similarity coefficient, 0.8495 ± 0.0392; relative volume difference, 15.2353 ± 17.2350; absolute relative volume difference, 18.8710 ± 13.1546; 95% Hausdorff distance, 7.2366 ± 1.8502; and average boundary distance, 2.2107 ± 0.4972; in parameters of rpxy = 10, rpz = 1, rsxy = 3, rsz = 1, and β = 3. Conclusion: The evaluated results showed the feasibility of the JLF algorithm for automatic segmentation of prostate MRI. This empirical analysis of segmentation results by label fusion allows for the appropriate setting of parameters.

Keywords

References

  1. Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin 2017;67:7-30 https://doi.org/10.3322/caac.21387
  2. Rosenkrantz AB, Oto A, Turkbey B, Westphalen AC. Prostate Imaging Reporting and Data System (PI-RADS), version 2: a critical look. AJR Am J Roentgenol 2016;206:1179-1183 https://doi.org/10.2214/AJR.15.15765
  3. Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS Prostate Imaging - Reporting and Data System: 2015, version 2. Eur Urol 2016;69:16-40 https://doi.org/10.1016/j.eururo.2015.08.052
  4. Yoon JM, Choi MH, Lee YJ, Jung SE. Dynamic contrastenhanced MRI of the prostate: can auto-generated wash-in color map be useful in detecting focal lesion enhancement? Investig Magn Reson Imaging 2019;23:220-227 https://doi.org/10.13104/imri.2019.23.3.220
  5. Choi MH, Jung SE, Park YH, Lee JY, Choi YJ. Multiparametric MRI of prostate cancer after biopsy: little impact of hemorrhage on tumor staging. Investig Magn Reson Imaging 2017;21:139-147 https://doi.org/10.13104/imri.2017.21.3.139
  6. Giannini V, Mazzetti S, Vignati A, et al. A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging. Comput Med Imaging Graph 2015;46 Pt 2:219-226 https://doi.org/10.1016/j.compmedimag.2015.09.001
  7. Gao Y, Sandhu R, Fichtinger G, Tannenbaum AR. A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE Trans Med Imaging 2010;29:1781-1794 https://doi.org/10.1109/TMI.2010.2052065
  8. Ghose S, Oliver A, Marti R, et al. A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput Methods Programs Biomed 2012;108:262-287 https://doi.org/10.1016/j.cmpb.2012.04.006
  9. Tian Z, Liu L, Zhang Z, Fei B. Superpixel-based segmentation for 3D prostate MR images. IEEE Trans Med Imaging 2016;35:791-801 https://doi.org/10.1109/TMI.2015.2496296
  10. Klein S, van der Heide UA, Lips IM, van Vulpen M, Staring M, Pluim JP. Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med Phys 2008;35:1407-1417 https://doi.org/10.1118/1.2842076
  11. Chandra SS, Dowling JA, Greer PB, et al. Fast automated segmentation of multiple objects via spatially weighted shape learning. Phys Med Biol 2016;61:8070-8084 https://doi.org/10.1088/0031-9155/61/22/8070
  12. Greenham S, Dean J, Fu CK, et al. Evaluation of atlas-based auto-segmentation software in prostate cancer patients. J Med Radiat Sci 2014;61:151-158 https://doi.org/10.1002/jmrs.64
  13. Litjens G, Toth R, van de Ven W, et al. Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med Image Anal 2014;18:359-373 https://doi.org/10.1016/j.media.2013.12.002
  14. Wang H, Suh JW, Das SR, Pluta JB, Craige C, Yushkevich PA. Multi-atlas segmentation with joint label fusion. IEEE Trans Pattern Anal Mach Intell 2013;35:611-623 https://doi.org/10.1109/TPAMI.2012.143
  15. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23 Suppl 1:S208-219 https://doi.org/10.1016/j.neuroimage.2004.07.051