6 June 2000 Knowledge-based segmentation of pediatric kidneys in CT for measuring parenchymal volume
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Abstract
The purpose of this work was to develop an automated method for segmenting pediatric kidneys in contrast-enhanced helical CT images and measuring the volume of the renal parenchyma. An automated system was developed to segment the abdomen, spine, aorta and kidneys. The expected size, shape, topology an X-ray attenuation of anatomical structures are stored as features in an anatomical model. These features guide 3-D threshold-based segmentation and then matching of extracted image regions to anatomical structures in the model. Following segmentation, the kidney volumes are calculated by summing included voxels. To validate the system, the kidney volumes of 4 swine were calculated using our approach and compared to the 'true' volumes measured after harvesting the kidneys. Automated volume calculations were also performed retrospectively in a cohort of 10 children. The mean difference between the calculated and measured values in the swine kidneys was 1.38 (S.D. plus or minus 0.44) cc. For the pediatric cases, calculated volumes ranged from 41.7 - 252.1 cc/kidney, and the mean ratio of right to left kidney volume was 0.96 (S.D. plus or minus 0.07). These results demonstrate the accuracy of the volumetric technique that may in the future provide an objective assessment of renal damage.
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Matthew S. Brown, Matthew S. Brown, Waldo C. Feng, Waldo C. Feng, Theodore R. Hall, Theodore R. Hall, Michael F. McNitt-Gray, Michael F. McNitt-Gray, Bernard M. Churchill, Bernard M. Churchill, "Knowledge-based segmentation of pediatric kidneys in CT for measuring parenchymal volume", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387679; https://doi.org/10.1117/12.387679
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