Knowledge-based segmentation of pediatric kidneys in CT for measurement of parenchymal volume.
OBJECTIVE
The purpose of this work was to develop an automated method for segmenting pediatric kidneys in helical CT images and measuring their volume.


METHODS
An automated system was developed to segment the kidneys. Parametric features of anatomic structures were used to guide segmentation and labeling of image regions. Kidney volumes were calculated by summing included voxels. For validation, the kidney volumes of four swine were calculated using our approach and compared with the "true" volumes measured after harvesting the kidneys. Automated volume calculations were also performed in a cohort of nine children.


RESULTS
The mean difference between the calculated and measured values in the swine kidneys was 1.38 ml. For the pediatric cases, calculated volumes ranged from 41.7 to 252.1 ml/kidney, and the mean ratio of right to left kidney volume was 0.96.


CONCLUSIONS
These results demonstrate the accuracy of a volumetric technique that may in the future provide an objective assessment of renal damage.
Brown
M S
MS
Department of Radiological Sciences, University of California, Los Angeles, CA 90095-1721, USA. mbrown@mednet.ucla.edu
Feng
W C
WC
Hall
T R
TR
McNitt-Gray
M F
MF
Churchill
B M
BM
D001185:Artificial Intelligence; D002648:Child; D002675:Child, Preschool; D003287:Contrast Media / Q000008:administration & dosage; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007668:Kidney / Q000033:anatomy & histology / Q000000981:diagnostic imaging* / Q000254:growth & development; D014057:Tomography, X-Ray Computed / Q000379:methods*
D003287:Contrast Media