Quantification of small cerebral ventricular volume changes in treated growth hormone patients using nonrigid registration.
IEEE TRANS MED IMAGING
(278-0062 (Print), 10)
1292 - 1301.
Nonrigid registration can automatically quantify small changes in volume of anatomical structures over time by means of segmentation propagation. Here, we use a nonrigid registration algorithm based on optimising normalized mutual information to quantify small changes in brain ventricle volume in magnetic resonance (MR) images of a group of five patients treated with growth hormone replacement therapy and a control group of six volunteers. The lateral ventricles are segmented from each subject image by registering with the brainweb image which has this structure delineated. The mean (standard deviation) volume change measurements are 1.09 (0.73) cm3 for the patient group and 0.08 (0.62) cm3 for the volunteer group; this difference is statistically significant at the 1% level. We validate our volume measurements by determining the precision from three consecutive scans of five volunteers and also comparing the measurements to previously published volume change estimates obtained by visual inspection of difference images. Results demonstrate a precision of sigma < or = 0.52 cm3 (n = 5) and a rank correlation coefficient with assessed difference images of p = 0.7 (n = 11). To determine the level of shape correspondence we manually segmented subject's ventricles and compared them to the propagations using a voxel overlap similarity index, this gave a mean similarity index of 0.81 (n = 7)
|Title:||Quantification of small cerebral ventricular volume changes in treated growth hormone patients using nonrigid registration|
|Additional information:||DA - 20030214|
|Keywords:||Algorithms, Anatomy, Cross-Sectional, methods, Cerebral Ventricles, drug effects, pathology, Comparative Study, Dwarfism, Pituitary, diagnosis, drug therapy, Hormone Replacement Therapy, Human Growth Hormone, administration & dosage, deficiency, Humans, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Microbial Sensitivity Tests, Observer Variation, Research Support, Non-U.S.Gov't, Subtraction Technique|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
UCL > School of BEAMS > Faculty of Engineering Science > Medical Physics and Bioengineering
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