Validation of an automated segmentation algorithm for lower leg MR images, applied to sodium quantification

Objective To develop and validate an automated segmentation algorithm for the lower leg using a multi-parametric magnetic resonance imaging protocol. Methods An automated algorithm combining active contour and intensity-based thresholding methods was developed to identify skin and muscle regions from proton Dixon MR images of the lower leg. Tissue sodium concentration was then computed using contemporaneously acquired sodium images with calibrated phantoms in the field of view. Resulting sodium concentration measurements were compared to a gold standard manual segmentation in 126 scans. Results Most cases had no observable errors in segmentation of muscle and skin. Six cases had minor errors that were not expected to affect quantification; in the worst, 126 mm2 (2%) of a muscle area of 8,042 mm2 was misclassified. In one case the algorithm failed to separate the tibia from the muscle compartment. Correlation between automated and manual measurements of sodium concentration was R2 = 0.84 for skin, R2 = 0.99 for muscle. Additionally, the RMSE was 2.4mM for skin and 0.5mM for muscle; the observed physiological range was 8.5 to 37.4mM. Conclusion For the purpose of estimating sodium concentrations in muscle and skin compartments, the automated segmentations provided equally accurate results compared to the more time-intensive manual segmentations. Sodium quantification serves as a biomarker for disease progression, which would assist with early diagnostic treatments. The proposed algorithm will improve workflow, reproducibility, and consistency in such studies.

• Publication year

  2018

• Venue

  bioRxiv

• Publication date

  2018-06-15

• Fields of study

  Biology, Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1101/348136
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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