Automated Region of Interest Detection Method in Scintigraphic Glomerular Filtration Rate Estimation

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.

Deep regression using 99mTc-DTPA dynamic renal imaging for automatic calculation of the glomerular filtration rate

OBJECTIVES

To develop and evaluate an artificial intelligence (AI) system that can automatically calculate the glomerular filtration rate (GFR) from dynamic renal imaging without manually delineating the regions of interest (ROIs) of kidneys and the corresponding background.

METHODS

This study was a single-center retrospective analysis of the data of 14,634 patients who underwent ^99mTc-DTPA dynamic renal imaging. Two systems based on convolutional neural networks (CNN) were developed and evaluated: sGFR_a predicts the radioactive counts of ROIs and calculates GFR using the Gates equation and sGFR_b directly predicts GFR from dynamic renal imaging without using other information. The root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R² were used to evaluate the performance of our approach.

RESULTS

sGFR_a achieved an RMSE of 5.05, MAE of 4.03, MAPE of 6.07%, and R² of 0.93 for total GFR while sGFR_b achieved an RMSE of 7.61, MAE of 5.92, MAPE of 8.92%, and R² of 0.85 for total GFR. The accuracy of sGFR_a and sGFR_b in determining the stage of chronic kidney disease was 87.41% and 82.44%, respectively.

CONCLUSIONS

The findings of sGFR_a show that automatic GFR calculation based on CNN and using dynamic renal imaging is feasible and efficient and, additionally, can aid clinical diagnosis. Furthermore, the promising results of sGFR_b demonstrate that CNN can predict GFR from dynamic renal imaging without additional information.

KEY POINTS

• Our CNN-based AI systems can automatically calculate GFR from dynamic renal imaging without manually delineating the ROIs of kidneys and the corresponding background. • sGFR_a accurately predicted the radioactive counts of ROIs and calculated GFR using the Gates method. • sGFR_b-predicted GFR directly without any parameters related to the Gates equation.

Relative Ectopic Kidney Function Quantification Using DMSA Tomoscintigraphy Modality

The ectopic renal function estimation based on a manual region of interest (ROI) extraction could be considered as time consuming. It could also affect the clinical interpretation and thus deviate the therapeutic attitude. For this purpose, we propose an advanced tool to evaluate such function through the dimercaptosuccinic acid (DMSA) kidney scintigraphy scans. Methods. The proposed study has been performed on one hundred patients (fifty cases with normal kidneys and fifty cases with ectopic kidneys). We present our segmentation problems as several cost functions' optimization, each containing two terms: (i) a distribution matching prior, which evaluates a global similarity between distributions, and (ii) a smoothness prior to avoid the occurrence of small, isolated regions in the solution. Obtained following recent bound-relaxation results, the optima of the cost functions yield each kidney region in near real time. The Dice Metric (DM), the Jaccard Index (JI), and the correlation parameter have been adopted as validation parameters in order to evaluate the segmentation results. The obtained relative function of both kidneys has been then compared with that evaluated in clinical routine (planar projection) and then validated statistically by the Bland-Altman plots and the Interclass Correlation Coefficient (ICC). Results. Compared to the expert's manual kidney segmentation, the obtained results have been judged to be acceptable for clinical use with high Mean Dice Metric (MDM) value and high Jaccard Index (JI). The evaluated relative renal function has been different from those calculated by the projection planar method usually used in clinical routines. Conclusion. The proposed system could efficiently extract the renal region. The relative function estimation could be considered as more accurate. In fact, the background noise correction and the attenuation phenomenon, which could yield an error measure for renal ectopia, have been avoided. Our clinical staff members have validated the results and have suggested using such tool in their clinical routines.