Automated total kidney volume measurements in pre-clinical magnetic resonance imaging for resourcing imaging data, annotations, and source code

Marie E. Edwards, Sigapriya Periyanan, Deema Anaam, Adriana V. Gregory, Timothy L. Kline

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The objective of this study was to validate a fully automated total kidney volume measurement method for pre-clinical rodent trials that is fast, accurate, reproducible, and to provide these resources to the research community. Rodent studies that involve imaging are crucial for monitoring treatment efficacy in diseases such as polycystic kidney disease. Previous studies utilize manual or semi-automated segmentations, which are time consuming and potentially biased. To develop our automated system, a total of 150 axial magnetic resonance images (MRI) from a variety of mouse models were manually segmented and used to train/validate an automated algorithm. To test the longitudinal application of the model, four mutant and four wild-type mice were imaged sequentially over three to twelve weeks via MRI. Segmentations of the kidneys (excluding the renal pelvis) were generated by the automated method and two different readers, with one reader repeating the measurements. Similarity metrics and longitudinal analysis were calculated to assess the performance of the automated compared to the manual methods. The automated approach required no user input, besides a final visual quality control step. Similarity metrics of the automated method versus the manual segmentations were on par with inter- and intra-reader comparisons. Thus, our fully automated approach described here can be safely used in longitudinal, pre-clinical trials that involve the segmentation of rodent kidneys in T2-weighted MRIs.

Original languageEnglish (US)
Pages (from-to)763-766
Number of pages4
JournalKidney international
Volume99
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • deep learning
  • murine
  • polycystic kidney disease
  • segmentation
  • similarity metrics
  • total kidney volume

ASJC Scopus subject areas

  • Nephrology

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