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Automatic segmentation of intra-abdominal and subcutaneous adipose tissue in 3D whole mouse MRI.
J Magn Reson Imaging. 2009 Sep; 30(3):554-60.JM

Abstract

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.

Authors+Show Affiliations

AstraZeneca R&D, DECS Imaging, Mölndal, Sweden.No affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

19711401

Citation

Ranefall, Petter, et al. "Automatic Segmentation of Intra-abdominal and Subcutaneous Adipose Tissue in 3D Whole Mouse MRI." Journal of Magnetic Resonance Imaging : JMRI, vol. 30, no. 3, 2009, pp. 554-60.
Ranefall P, Bidar AW, Hockings PD. Automatic segmentation of intra-abdominal and subcutaneous adipose tissue in 3D whole mouse MRI. J Magn Reson Imaging. 2009;30(3):554-60.
Ranefall, P., Bidar, A. W., & Hockings, P. D. (2009). Automatic segmentation of intra-abdominal and subcutaneous adipose tissue in 3D whole mouse MRI. Journal of Magnetic Resonance Imaging : JMRI, 30(3), 554-60. https://doi.org/10.1002/jmri.21874
Ranefall P, Bidar AW, Hockings PD. Automatic Segmentation of Intra-abdominal and Subcutaneous Adipose Tissue in 3D Whole Mouse MRI. J Magn Reson Imaging. 2009;30(3):554-60. PubMed PMID: 19711401.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Automatic segmentation of intra-abdominal and subcutaneous adipose tissue in 3D whole mouse MRI. AU - Ranefall,Petter, AU - Bidar,Abdel Wahad, AU - Hockings,Paul D, PY - 2009/8/28/entrez PY - 2009/8/28/pubmed PY - 2009/10/21/medline SP - 554 EP - 60 JF - Journal of magnetic resonance imaging : JMRI JO - J Magn Reson Imaging VL - 30 IS - 3 N2 - 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. SN - 1053-1807 UR - https://www.unboundmedicine.com/medline/citation/19711401/Automatic_segmentation_of_intra_abdominal_and_subcutaneous_adipose_tissue_in_3D_whole_mouse_MRI_ L2 - https://doi.org/10.1002/jmri.21874 DB - PRIME DP - Unbound Medicine ER -