一种医学肾动态显像自动化定量评估方法

doi:10.11896/j.issn.1002-137X.2019.08.053

Abstract

Abstract: The evaluation method of renal function in clinical renal dynamic imaging depends too much on manual acquisition of ROI (Region of Interest)and has low time efficiency.In order to solve this problem,this paper proposed anautomatic quantitative assessment method for medical renal dynamic imaging.Firstly,the images of renal dynamic imaging at different stages are pretreated.Secondly,an improved level set model is utilized to obtain the ROI of the renal function imaging.The ROI is obtained by morphological methods,then the ROI of the aorta in the renal perfusion imaging is located and obtained.Finally,GFR(Glomerular Filtration Rate) is calculated according to the Gates method,and the time-radioactivity curve is plotted based on the radioactivity counts in ROI,so as to achieve integrated and automated assessment for renal function.The results of clinical trials show that the proposed automatic assessment method can improve the automation level in a short period of time and raise the assessment accuracy,which provide effective help for clinical diagnosis and adjuvant treatment

Key words: Automatic quantitative assessment, Glomerular filtration rate, Region of interest, Renal dynamic imaging

CLC Number:

TP391.41

CHAI Rui, XUE Fan, ZENG Jian-chao, QIN Pin-le. Automatic Quantitative Evaluation Approach for Medical Renal Dynamic Imaging[J].Computer Science, 2019, 46(8): 321-326.

References

[1]YORUK U,HARGREAVES B A,VASANAWALA S S.Automatic renal segmentation for MR urography using 3D-GrabCut and random forests[J].Magnetic Resonance in Medicine,2018,79(3):1696-1707.
[2]TORRES H R,QUEIRÒS S,MORAIS P,et al.Kidney Segmentation in Ultrasound,Magnetic Resonance and Computed Tomography Images:A Systematic Review[J].Computer Methods & Programs in Biomedicine,2018,157:49-67.
[3]LJUNGBERG B,BENSALAH K,CANFIELD S,et al.EAU guidelines on renal cell carcinoma:2014 update[J].European Urology,2015,67(5):913-924.
[4]TAN H J,NORTON E C,YE Z,et al.Long-term Survival Following Partial vs Radical Nephrectomy Among Older Patients With Early-Stage Kidney Cancer[J].Jama,2012,307(15):1629-1635.
[5]GATES G F.Glomerular filtration rate:estimation from fractional renal accumulation of 99mTc-DTPA (stannous)[J].American Journal of Roentgenology,1982,138(3):565-570.
[6]GARCIA E V,FOLKS R,PAK S,et al.Totally automatic definition of renal regions of interest from 99mTc-MAG3 renograms:validation in patients with normal kidneys and in patients with suspected renal obstruction[J].Nuclear Medicine Communications,2010,31(5):366-374.
[7]TIAN S C,XU L,JIANG H B.A Research on the Automatic ROI Delineate Method of Renal Imaging Based on Morphological Reconstruction and Otsu Thresholding Segmentation[J].China Medical Devices,2016,31(2):44-47.(in Chinese) 田书畅,徐磊,蒋红兵.基于肾动态定量分析中肾脏ROI的自动勾画研究[J].中国医疗设备,2016,31(2):44-47
[8]LANDGREN M,SJÖSTRAND K,OHLSSON M,et al.An Automated System for the Detection and Diagnosis of Kidney Lesions in Children from Scintigraphy Images[C]∥Scandinavian Conference on Image Analysis.Springer-Verlag,2011:489-500.
[9]LIU R C,XU L,ZHANG L L.Study of a Novel Automatic Method of ROI Delineation in Renal Dynamic Radionuclide Imaging[J].China Medical Devices,2017,32(12):68-71.(in Chinese) 刘任从,徐磊,张乐乐.核素肾动态显像肾脏感兴趣区域分割算法的研究[J].中国医疗设备,2017,32(12):68-71.
[10]JIANG G P,QIN W J,ZHOU S J,et al.State-of-the-Art in Medical Image Segmentation[J].Chinese Journal of Compu-ters,2015,38(6):1222-1242.(in Chinese) 江贵平,秦文健,周寿军,等.医学图像分割及其发展现状[J].计算机学报,2015,38(6):1222-1242.
[11]WANG X,GUO L,YIN J,et al.Narrowband Chan-Vese model of sonar image segmentation:A adaptive ladder initialization approach[J].Applied Acoustics,2016,113:238-254.
[12]ZHANG P,LIANG Y M,CHANG S J,et al.Kidney segmentation in computed tomography sequences based on energy minimization[J].Acta Physica Sinica,2013,62(20):208701.(in Chinese) 张品,梁艳梅,常胜江,等.基于能量最小化的肾脏计算断层扫描图像分割方法[J].物理学报,2013,62(20):208701.
[13]SHI Y G,TAN J S,LIU Z W.Renal Cortex Segmentation Using Graph Cuts and Level Sets[J].Computer Science,2016,43(7):290-293.(in Chinese) 时永刚,谭继双,刘志文.基于图割和水平集的肾脏医学图像分割[J].计算机科学,2016,43(7):290-293.
[14]QIAN S,WENG G.Medical image segmentation based on FCM and Level Set algorithm[C]∥IEEE International Conference on Software Engineering and Service Science.IEEE,2017:225-228.
[15]TIAN C,ZHANG X,HAN Y,et al.A semi-automated region of interest detection method in the scintigraphic glomerular filtration rate determination for patients with abnormal low renal function[J].Clinical Nuclear Medicine,2013,38(11):855-862.
[16]LIN K J,HUANG J Y,CHEN Y S.Fully automatic region of interest selection in glomerular filtration rate estimation from 99m tc-dtpa renogram[J].Journal of Digital Imaging,2011,24(6):1010-1023.
[17]HUANG C,ZENG L.Level set evolution model for image segmentation based on variable exponent p-Laplace equation[J].Applied Mathematical Modelling,2016,40(17／18):7739-7750.
[18]LIU C,LIU W,XING W.An improved edge-based level set method combining local regional fitting information for noisy image segmentation[J].Signal Processing,2017,130:12-21.
[19]LI C,XU C,GUI C,et al.Level Set Evolution without Re-Initialization:A New Variational Formulation[C]∥IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition,2005(CVPR 2005).IEEE Xplore,2005:430-436.
[20]WANG C Y,ZHAO Y F.Influence of different outlining for region of interest in one kidney with Gates method on glomerular filtration rate of the contralateral kidney[J].Chinese Journal of Nuclear Medicine and Molecular Imaging,2016,36(5):464-465.(in Chinese) 汪长银,赵燕芬.Gates法测肾功能时一侧肾脏感兴趣区勾画面积对另一侧肾小球滤过率的影响[J].中华核医学与分子影像杂志,2016,36(5):464-465.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Automatic Quantitative Evaluation Approach for Medical Renal Dynamic Imaging

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 10

Metrics

Comments

Recommended 0

[1]	HE Xia, TANG Yi-ping, WANG Li-ran, CHEN Peng, YUAN Gong-ping. Multitask Hierarchical Image Retrieval Technology Based on Faster RCNNH [J]. Computer Science, 2019, 46(3): 303-313.
[2]	LIU Gang, ZHANG Jing, LI Yue-long. Extraction of Palm Vein ROI Based on Maximal Inscribed Circle Algorithm [J]. Computer Science, 2018, 45(8): 264-267.
[3]	ZHAO Yang, WANG Wei, DONG Rong, WANG Jing-shi and TANG Min. Compressed Sensing Recovery Algorithm for Region of Interests of MRI/MRA Images Based on NLTV and NESTA [J]. Computer Science, 2017, 44(9): 308-314.
[4]	ZHOU Yi-hua, WANG Wen-dong, CHEN Hong-cai, WANG Ting and ZHANG Chang-you. Research on Parallel Algorithm of Vehicle Image Interested Region Based on Pthreads [J]. Computer Science, 2017, 44(3): 36-37.
[5]	PAN Ming-xing and SUN Han. Fast and Efficient Algorithm for Airborne Target Recognition [J]. Computer Science, 2014, 41(Z6): 150-152.
[6]	TANG Min,CHEN Xiu-mei and CHEN Feng. Colorful Medical Image Compression Based on Contourlet Transform and SPIHT Algorithm [J]. Computer Science, 2014, 41(1): 303-306.
[7]	XIA Chun-yu,WANG Xiang-hai. ROI Coding Method Based on Selecting Subband and Bitplane Shift [J]. Computer Science, 2009, 36(12): 272-277.
[8]	RUAN Ruo-lin, HU Rui-min, LI Zhong-ming. Research on RD Optimized Intra Refreshment Algorithm for Region of Interest [J]. Computer Science, 2009, 36(10): 284-288.
[9]	. [J]. Computer Science, 2008, 35(5): 257-259.
[10]	. [J]. Computer Science, 2008, 35(4): 233-237.