A semi-automated “blanket” method for renal segmentation from non-contrast T1-weighted MR images

Henry Rusinek, Jeremy C. Lim, Nicole Wake, Jas mine Seah, Elissa Botterill, Shawna Farquharson, Artem Mikheev, Ruth P. Lim

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Objective: To investigate the precision and accuracy of a new semi-automated method for kidney segmentation from single-breath-hold non-contrast MRI. Materials and methods: The user draws approximate kidney contours on every tenth slice, focusing on separating adjacent organs from the kidney. The program then performs a sequence of fully automatic steps: contour filling, interpolation, non-uniformity correction, sampling of representative parenchyma signal, and 3D binary morphology. Three independent observers applied the method to images of 40 kidneys ranging in volume from 94.6 to 254.5 cm3. Manually constructed reference masks were used to assess accuracy. Results: The volume errors for the three readers were: 4.4 % ± 3.0 %, 2.9 % ± 2.3 %, and 3.1 % ± 2.7 %. The relative discrepancy across readers was 2.5 % ± 2.1 %. The interactive processing time on average was 1.5 min per kidney. Conclusions: Pending further validation, the semi-automated method could be applied for monitoring of renal status using non-contrast MRI.

Original languageEnglish (US)
Pages (from-to)197-206
Number of pages10
JournalMagnetic Resonance Materials in Physics, Biology and Medicine
Volume29
Issue number2
DOIs
StatePublished - Apr 1 2016
Externally publishedYes

Keywords

  • Kidney
  • MRI
  • Renal
  • Segmentation
  • Volume

ASJC Scopus subject areas

  • Biophysics
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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