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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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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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  4. Mahasti Amoui
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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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  • DOI: https://doi.org/10.1007/s11517-022-02717-7

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