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A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy

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Abstract

Objective

Accurate delineation of renal regions of interest (ROIs) is critical for the assessment of renal function in pediatric dynamic renal scintigraphy (DRS). The purpose of this study was to develop and evaluate a deep learning (DL) model that can fully automatically delineate renal ROIs and calculate renal function in pediatric 99mTechnetium-ethylenedicysteine (99mTc-EC) DRS.

Methods

This study retrospectively analyzed 1,283 pediatric DRS data at a single center from January to December 2018. These patients were divided into training set (n = 1027), validation set (n = 128), and testing set (n = 128). A fully automatic segmentation of ROIs (FASR) model was developed and evaluated. The pixel values of the automatically segmented ROIs were calculated to predict renal blood perfusion rate (BPR) and differential renal function (DRF). Precision, recall rate, intersection over union (IOU), and Dice similarity coefficient (DSC) were used to evaluate the performance of FASR model. Intraclass correlation (ICC) and Pearson correlation analysis were used to compare the consistency of automatic and manual method in assessing the renal function parameters in the testing set.

Results

The FASR model achieved a precision of 0.88, recall rate of 0.94, IOU of 0.83, and DSC of 0.91. In the testing set, the r values of BPR and DRF calculated by the two methods were 0.94 (P < 0.01) and 0.97 (P < 0.01), and the ICCs (95% confidence interval CI) were 0.94 (0.90—0.96) and 0.94 (0.91—0.96).

Conclusion

We propose a reliable and stable DL model that can fully automatically segment ROIs and accurately predict renal function in pediatric 99mTc-EC DRS.

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Data availability

Data are contained within the article, and the datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.

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Acknowledgements

We would like to thank the staff of the department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine.

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Author notes

  1. Xueli Ji, Guohui Zhu and Jinyu Gou have contributed equally contributed.

Authors and Affiliations

  1. Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China

    Xueli Ji, Jinyu Gou, Suyun Chen, Wenyu Zhao, Hongliang Fu & Hui Wang

  2. Institute of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200092, China

    Guohui Zhu & Zhanquan Sun

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  1. Xueli Ji
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Correspondence to Zhanquan Sun, Hongliang Fu or Hui Wang.

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Ji, X., Zhu, G., Gou, J. et al. A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy. Ann Nucl Med 38, 382–390 (2024). https://doi.org/10.1007/s12149-024-01907-7

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