A knowledge-guided active model method of cortical structure segmentation on pediatric MR images.
OBJECTIVE
To develop an automated method for quantification of cortical structures on pediatric MR images.


METHODS
A knowledge-guided active model (KAM) approach was proposed with a novel object function similar to the Gibbs free energy function. Triangular mesh models were transformed to images of a given subject by maximizing entropy, and then actively slithered to boundaries of structures by minimizing enthalpy. Volumetric results and image similarities of 10 different cortical structures segmented by KAM were compared with those traced manually. Furthermore, the segmentation performances of KAM and SPM2, (statistical parametric mapping, a MATLAB software package) were compared.


RESULTS
The averaged volumetric agreements between KAM- and manually-defined structures (both 0.95 for structures in healthy children and children with medulloblastoma) were higher than the volumetric agreement for SPM2 (0.90 and 0.80, respectively). The similarity measurements (kappa) between KAM- and manually-defined structures (0.95 and 0.93, respectively) were higher than those for SPM2 (both 0.86).


CONCLUSIONS
We have developed a novel automatic algorithm, KAM, for segmentation of cortical structures on MR images of pediatric patients. Our preliminary results indicated that when segmenting cortical structures, KAM was in better agreement with manually-delineated structures than SPM2. KAM can potentially be used to segment cortical structures for conformal radiation therapy planning and for quantitative evaluation of changes in disease or abnormality.
Shan
Zuyao Y
ZY
Division of Translational Imaging Research, Department of Radiological Sciences, St. Jude Children's Research Hospital, and Department of Biomedical Engineering, The University of Memphis, Tennessee 381005, USA. Zuyao.shan@stjude.org
Parra
Carlos
C
Ji
Qing
Q
Jain
Jinesh
J
Reddick
Wilburn E
WE
D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002648:Child; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging