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

IF 2.5 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Annals of Nuclear Medicine Pub Date : 2024-02-20 DOI:10.1007/s12149-024-01907-7

Xueli Ji, Guohui Zhu, Jinyu Gou, Suyun Chen, Wenyu Zhao, Zhanquan Sun, Hongliang Fu, Hui Wang

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引用次数: 0

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.

Abstract Image

查看原文本刊更多论文

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。

目的：准确划分肾脏感兴趣区（ROI）对于评估小儿动态肾脏闪烁成像（DRS）中的肾功能至关重要。本研究的目的是开发和评估一种深度学习（DL）模型，该模型可在小儿 99m锝- 乙二半胱氨酸（99mTc-EC）DRS 中全自动划定肾脏 ROI 并计算肾功能：本研究回顾性分析了 2018 年 1 月至 12 月一个中心的 1283 例小儿 DRS 数据。这些患者被分为训练集（n = 1027）、验证集（n = 128）和测试集（n = 128）。开发并评估了 ROI 全自动分割（FASR）模型。通过计算自动分割 ROI 的像素值来预测肾脏血液灌注率（BPR）和肾功能差异（DRF）。精确度、召回率、交集大于联合（IOU）和 Dice 相似系数（DSC）用于评估 FASR 模型的性能。类内相关（ICC）和皮尔逊相关分析用于比较自动和人工方法在评估测试集中肾功能参数的一致性：FASR 模型的精确度为 0.88，召回率为 0.94，IOU 为 0.83，DSC 为 0.91。在测试集中，两种方法计算出的 BPR 和 DRF 的 r 值均为 0.94（P 结论：FASR 模型的精确度、召回率和 DSC 分别为 0.88、0.83 和 0.91：我们提出了一种可靠、稳定的 DL 模型，该模型可在小儿 99mTc-EC DRS 中全自动分割 ROI 并准确预测肾功能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annals of Nuclear Medicine 医学-核医学

CiteScore

4.90

自引率

7.70%

发文量

111

审稿时长

4-8 weeks

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.