ArticleNuclear medicine communications2010
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.
Article in Nuclear medicine communications, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 28 citations in OpenAlex.
- A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy.Annals of nuclear medicine · 2024Article
- Point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy.Scientific reports · 2023Article
- An automatic segmentation framework for computer-assisted renal scintigraphy procedure.Medical & biological engineering & computing · 2023Article
- Relative Ectopic Kidney Function Quantification Using DMSA Tomoscintigraphy Modality.Journal of healthcare engineering · 2020Article
- [A computer tomography assisted method for the automatic detection of region of interest in dynamic kidney images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2017Article
- Computer-assisted diagnosis in renal nuclear medicine: rationale, methodology, and interpretative criteria for diuretic renography.Seminars in nuclear medicine · 2014Review
- Reference values for renal size obtained from MAG3 scintigraphy.Clinical nuclear medicine · 2013Article
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4 authors at 3 institutions in 1 country.
Funding
Abstract
introductionAn image-processing algorithm (AUTOROI) has been developed to totally automatically (or with manual assistance) detect whole-kidney contours and generate renal regions of interest (ROI) for the extraction of the quantitative measurements used in the interpretation of Tc-mercaptoacetyltriglycine (Tc-MAG3) renograms.
methodsThe 18-20th min dynamic frames post-MAG3 injection were used to automatically define boxes surrounding each kidney, which were then transposed to an early composite image for interpolative and directional background subtraction. Sobel operator and unsharp masking were applied for edge enhancement, and the resulting image histograms were equalized to better define poorly functioning kidneys. AUTOROI searched radially from the center of mass to define each kidney's ROI coordinates. AUTOROI was validated using MAG3 studies from 79 patients referred for suspected obstruction (79 left, 77 right kidneys) and 19 kidney donors with normal kidney function and no obstruction. Renal ROIs were manually defined by a nuclear medicine technologist with 20+ years of experience (reference standard) and an American Board of Nuclear Medicine certified physician. AUTOROI and physician ROIs were automatically compared with the reference standard to determine the border definition error.
resultsAUTOROI totally automatically detected the renal borders in 89% (172 of 194) of the kidneys from the entire group of 98 patients. The 22 kidneys missed automatically were subsequently detected with the assistance of a single manually placed fiducial point demarcating the liver/kidney boundary. These 22 kidneys were shown to be associated with markedly reduced MAG3 clearance. The mean error of AUTOROI for all 194 kidneys was 6.66+/-3.77 and 7.31+/-4.52 mm for the left and right kidney, respectively. The physician's error was 6.78+/-2.42 and 6.65+/-2.05 mm for the left and right kidney, respectively. This error difference between AUTOROI and the physician was not statistically significant.
conclusionAUTOROI provides an objective and promising approach to automated renal ROI detection.
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