Literature DB >> 21563253

Segmentations of MRI images of the female pelvic floor: a study of inter- and intra-reader reliability.

Lennox Hoyte1, Wen Ye, Linda Brubaker, Julia R Fielding, Mark E Lockhart, Marta E Heilbrun, Morton B Brown, Simon K Warfield.   

Abstract

PURPOSE: To describe the inter- and intra-operator reliability of segmentations of female pelvic floor structures.
MATERIALS AND METHODS: Three segmentation specialists were asked to segment out the female pelvic structures in 20 MR datasets on three separate occasions. The STAPLE algorithm was used to compute inter- and intra-segmenter agreement of each organ in each dataset. STAPLE computed the sensitivity, specificity, and positive predictive values (PPV) for inter- and intra-segmenter repeatability. These parameters were analyzed using intra-class correlation analysis. Correlation of organ volume to PPV and sensitivity was also computed.
RESULTS: Mean PPV of the segmented organs ranged from 0.82 to 0.99, and sensitivity ranged from 33 to 96%. Intra-class correlation ranged from 0.07 to 0.98 across segmenters. Pearson correlation of volume to sensitivity were significant across organs, ranging from 0.54 to 0.91. Organs with significant correlation of PPV to volume were bladder (-0.69), levator ani (-0.68), and coccyx (-0.63).
CONCLUSION: Undirected manual segmentation of the pelvic floor organs are adequate for locating the organs, but poor at defining structural boundaries.
Copyright © 2011 Wiley-Liss, Inc.

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Mesh:

Year:  2011        PMID: 21563253      PMCID: PMC4364418          DOI: 10.1002/jmri.22478

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  12 in total

1.  MR-based three-dimensional modeling of the normal pelvic floor in women: quantification of muscle mass.

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  14 in total

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5.  Three-dimensional self super-resolution for pelvic floor MRI using a convolutional neural network with multi-orientation data training.

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6.  A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images.

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9.  Biomechanical Mapping of the Female Pelvic Floor: Uterine Prolapse Versus Normal Conditions.

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10.  Predictive Value of Biomechanical Mapping for Pelvic Organ Prolapse Surgery.

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