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Automatic segmentation of intra‐abdominal and subcutaneous adipose tissue in 3D whole mouse MRI
Author(s) -
Ranefall Petter,
Bidar Abdel Wahad,
Hockings Paul D.
Publication year - 2009
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21874
Subject(s) - segmentation , voxel , computer science , artificial intelligence , adipose tissue , pipeline (software) , computer vision , image segmentation , pattern recognition (psychology) , medicine , programming language , endocrinology
Purpose To fully automate intra‐abdominal (IAT) and total adipose tissue (TAT) segmentation in mice to replace tedious and subjective manual segmentation. Materials and Methods A novel transform codes each voxel with the radius of the narrowest passage on the widest possible three‐dimensional (3D) path to any voxel in the target object to select appropriate IAT seed points. Then competitive region growing is performed on a distance transform of the fat mask such that competing classes meet at narrow passages effectively segmenting the IAT and subcutaneous adipose compartments. Fully automatic segmentations were conducted on 32 3D mouse images independent to those used for algorithm development. Results Automatic processing worked on all 32 images and took 28 s on a 3.6 GHz Pentium computer with 2.0 GB RAM. Manual segmentation by an experienced operator typically took 1 h per 3D image. The correlation coefficients between manual and automated segmentation of TAT and IAT were 0.97 and 0.94, respectively. Conclusion The fully automatic method correlates well with manual segmentation and dramatically speeds up segmentation allowing MRI to be used in the anti‐obesity drug discovery pipeline. J. Magn. Reson. Imaging 2009;30:554–560. © 2009 Wiley‐Liss, Inc.