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An automatic segmentation framework for computer-assisted renal scintigraphy procedure

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Abstract

One of the techniques for achieving unique and reliable information in medicine is renal scintigraphy. A key step for quantitative renal scintigraphy is segmentation of the kidneys. Here, an automatic segmentation framework was proposed for computer-aided renal scintigraphy procedures. To extract kidney boundary in dynamic renal scintigraphic images, a multi-step approach was proposed. This technique is featured with key steps, namely, localization and segmentation. At first, the ROI of each kidney was estimated using Otsu’s thresholding, anatomical constraint, and integral projection, which is done in an automatic process. Afterwards, the ROI obtained for the kidneys was used as the initial contours to create the final counter of kidneys using geometric active contours. At this step and for the segmentation, an improved variational level set was utilized through Mumford-Shah formulation. Using e.cam gamma camera system (SIEMENS), 30 data sets were used to assess the proposed method. By comparing the manually outlined borders, the performance of the proposed method was shown. Different measures were used to examine the performance. It was found that the proposed segmentation method managed to extract the kidney boundary in renal scintigraphic images. The proposed technique achieved a sensitivity of 95.15% and a specificity of 95.33%. In addition, the section under the curve in the ROC analysis was equal to 0.974. The proposed technique successfully segmented the renal contour in dynamic renal scintigraphy. Using all the data sets, a correct segmentation of the kidney was performed. In addition, the technique was successful with noisy and low-resolution images and challenging cases with close interfering activities such as liver and spleen activities.

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References

  1. Taylor A Jr, Nally JV (1995) Clinical applications of renal scintigraphy. Am J Roentgenol 164(1):31–41

    Article  Google Scholar 

  2. Mititelu R, Bratu O (2017) Radionuclide imaging an update on the use of dynamic renal scintigraphy. Mod Med 24(4):199–203

    Article  Google Scholar 

  3. Caglar M, Gedik GK, Karabulut E (2008) Differential renal function estimation by dynamic renal scintigraphy: influence of background definition and radiopharmaceutical. Nucl Med Commun 29(11):1002–1005

  4. Kaur R, Juneja M (2018) A survey of kidney segmentation techniques in CT images. Curr Med Imaging Rev 14(2):238–250

    Article  Google Scholar 

  5. Khalifa F, Elnakib A, Beache GM, Gimel’farb G, El-Ghar MA, Ouseph R, Sokhadze G, Manning S, McClure P, and El-Baz A (2011) 3D kidney segmentation from CT images using a level set approach guided by a novel stochastic speed function, Proc. 14th Int. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 587–594

  6. Gloger O, Tönnies KD, Liebscher V, Kugelmann B, Laqua R, Völzke H (2012) Prior shape level set segmentation on multistep generated probability maps of MR datasets for fully automatic kidney parenchyma volumetry. IEEE Trans Med Imaging 31(2):312–325

    Article  Google Scholar 

  7. Selvathia D, Bamab S (2017) Phase based distance regularized level set for the segmentation of ultrasound kidney images. Pattern Recogn Lett 86:9–17

    Article  Google Scholar 

  8. Garcia EV, Folks R, Pak S, Taylor A (2010) Totally automatic definition of renal regions-of-interest from Tc-99m MAG3 renograms: validation in patients with normal kidneys and in patients with suspected renal obstruction. Nucl Med Commun 31(5):366–374

    Article  Google Scholar 

  9. Landgren M, Sjostrand K, Ohlsson M, Stahl D, Overgaard NC, Astrom K, Sixt R, Edenbrandt L (2011) An automated system for the detection and diagnosis of kidney lesions in children from scintigraphy images. Lect Notes Comput Sci. Image Analys 6688:489–500

  10. Aribi Y, Wali A, Chakroun M, M.Alimi A (2013) Automatic definition of regions of interest on renal scintigraphic images. AASRI Conference on Intelligent Systems and Control Procedia 4:37–42

    Article  Google Scholar 

  11. Aribi Y, Hamza F, Wali A, Alimi AM, Guermazi F (2014) An automated system for the segmentation of dynamic scintigraphic images. Applied Medical Informatics 34(2):1–12

    Google Scholar 

  12. Aribi Y, Wali A, Alimi AM (2015) Automated fast marching method for segmentation and tracking of region of interest in scintigraphic Images sequences, Proc. 16th Int. Conf. Computer Analysis of Images and Patterns (CAIP) 9257:725–736

  13. Shiva A, Sankar Sai SS, Rajamani K, Subramaniyan VS (2015) A study on segmentation of renal scintigraphy using level sets algorithm, Third International Conference on Image Information Processing (ICIIP) 290–294

  14. Park J, Bae S, Seo S, Park S, Bang J, Han JH, Lee WW, Lee JS (2018) Deep learning based kidney segmentation for glomerular filtration rate measurement using quantitative SPECT/CT. J Nucl Med (JNM) 59(1)–26

  15. Park J, Bae S, Seo S, Park S, Bang J, Han JH, Lee WW and Lee JS (2019) Measurement of glomerular filtration rate using quantitative SPECT/CT and deep-learning-based kidney segmentation, scientific reports, 9 (1). https://doi.org/10.1038/s41598-019-40710-7

  16. Warfield S, Dengler J, Zaers J, Guttmann C, Gil W, Ettinger G, Hiller J, Kikinis R (1995) Automatic identification of grey matter structures from MRI to improve the segmentation of white matter lesions. J Image Guid Surg 1(6):326–338

    Article  Google Scholar 

  17. Hosntalab M, Babapour-Mofrad F, Monshizadeh N, Amoui M (2012) Automatic left ventricle segmentation in volumetric SPECT data set by variational Level Set. Int J Comput Assist Radiol Surg 7(6):837–843

    Article  Google Scholar 

  18. Jain AK, Chen H (2004) Matching of dental X-ray images for human identification. J Pattern Recognit 37:1519–1532

    Article  Google Scholar 

  19. Otsu N (1979) A threshold selection method from gray-level histograms”. IEEE Trans Syst Man Cyben 9(1):62–66

    Article  Google Scholar 

  20. Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edition. Prentice Hall, New Jersey

  21. Kass M, Witkin A, Terozopolous D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331

    Article  Google Scholar 

  22. Yezzi A, Kichenassamy S, Kumar A, Olver P, Tannenbaum A (1997) A geometric snake model for segmentation of medical imagery. IEEE Trans Med Imag 16:199–209

    Article  Google Scholar 

  23. Delingette H, Montagnat J (2001) Shape and topology constraints on parametric active contours. Comput Vis Image Underst 83:140–171

    Article  Google Scholar 

  24. Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comp Physics 79:12–49

    Article  Google Scholar 

  25. Zhao HK, Chan TF, Merriman B, Osher S (1996) A variational level set approach to multiphase motion. J Comput Phys 127(1):79–195

    Article  Google Scholar 

  26. Vese L, Chan T (2002) A multiphase level set framework for image segmentation using the Mumford and Shah model. Int J Comput Vision 50(3):271–293

    Article  Google Scholar 

  27. Hosntalab M, Zoroofi RA, Abbaspour Tehrani-Fard A, Shirani G (2008) Segmentation of teeth in CT volumetric data set by panoramic projection and variational level set. Int J CARS 3(3–4):257–265

    Article  Google Scholar 

  28. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

  29. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognt Letters 27(8):861–874

    Article  Google Scholar 

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Acknowledgements

The authors wish to express their gratitude towards the Division of Nuclear Medicine Imaging at Shohada-e-Tajrish Hospital for the data set and their valuable comments.

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Authors and Affiliations

  1. Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Arghavan Rahimi, Mohammad Hosntalab & Farshid Babapour Mofrad

  2. Department of Nuclear Medicine, Shohada-e Tajrish Hospital, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mahasti Amoui

  3. Machine and Hybrid Intelligence lab, Department of Radiology, Feinberg school of Medicine, Northwestern University, Chicago, IL, USA

    Ulas Bagci

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  1. Arghavan Rahimi
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  2. Mohammad Hosntalab
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  3. Farshid Babapour Mofrad
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Correspondence to Mohammad Hosntalab.

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Rahimi, A., Hosntalab, M., Babapour Mofrad, F. et al. An automatic segmentation framework for computer-assisted renal scintigraphy procedure. Med Biol Eng Comput 61, 285–295 (2023). https://doi.org/10.1007/s11517-022-02717-7

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