Xueli Ji, Guohui Zhu, Jinyu Gou, Suyun Chen, Wenyu Zhao, Zhanquan Sun, Hongliang Fu, Hui Wang
{"title":"基于深度学习的全自动方法，用于在儿科动态肾脏闪烁扫描中分割感兴趣区和预测肾功能。","authors":"Xueli Ji, Guohui Zhu, Jinyu Gou, Suyun Chen, Wenyu Zhao, Zhanquan Sun, Hongliang Fu, Hui Wang","doi":"10.1007/s12149-024-01907-7","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>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 <sup>99m</sup>Technetium-ethylenedicysteine (<sup>99m</sup>Tc-EC) DRS.</p><h3>Methods</h3><p>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 (<i>n</i> = 1027), validation set (<i>n</i> = 128), and testing set (<i>n</i> = 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.</p><h3>Results</h3><p>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 (<i>P</i> < 0.01) and 0.97 (<i>P</i> < 0.01), and the ICCs (95% confidence interval CI) were 0.94 (0.90—0.96) and 0.94 (0.91—0.96).</p><h3>Conclusion</h3><p>We propose a reliable and stable DL model that can fully automatically segment ROIs and accurately predict renal function in pediatric <sup>99m</sup>Tc-EC DRS.</p></div>","PeriodicalId":8007,"journal":{"name":"Annals of Nuclear Medicine","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy\",\"authors\":\"Xueli Ji, Guohui Zhu, Jinyu Gou, Suyun Chen, Wenyu Zhao, Zhanquan Sun, Hongliang Fu, Hui Wang\",\"doi\":\"10.1007/s12149-024-01907-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>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 <sup>99m</sup>Technetium-ethylenedicysteine (<sup>99m</sup>Tc-EC) DRS.</p><h3>Methods</h3><p>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 (<i>n</i> = 1027), validation set (<i>n</i> = 128), and testing set (<i>n</i> = 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.</p><h3>Results</h3><p>The FASR model achieved a precision of 0.88, recall rate of 0.94, IOU of 0.83, and DSC of 0.91. 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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
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.
期刊介绍：
Annals of Nuclear Medicine is an official journal of the Japanese Society of Nuclear Medicine. It develops the appropriate application of radioactive substances and stable nuclides in the field of medicine.
The journal promotes the exchange of ideas and information and research in nuclear medicine and includes the medical application of radionuclides and related subjects. It presents original articles, short communications, reviews and letters to the editor.
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