MRI-based thalamic volumetry in multiple sclerosis using FSL-FIRST: Systematic assessment of common error modes

J Neuroimaging. 2022 Mar;32(2):245-252. doi: 10.1111/jon.12947. Epub 2021 Nov 12.

Abstract

Background and purpose: FSL's FMRIB's Integrated Registration and Segmentation Tool (FSL-FIRST) is a widely used and well-validated tool. Automated thalamic segmentation is a common application and an important longitudinal measure for multiple sclerosis (MS). However, FSL-FIRST's algorithm is based on shape models derived from non-MS groups. As such, the present study sought to systematically assess common thalamic segmentation errors made by FSL-FIRST on MRIs from people with multiple sclerosis (PwMS).

Methods: FSL-FIRST was applied to generate thalamic segmentation masks for 890 MR images in PwMS. Images and masks were reviewed systematically to classify and quantify errors, as well as associated anatomical variations and MRI abnormalities. For cases with overt errors (n = 362), thalamic masks were corrected and quantitative volumetric differences were calculated.

Results: In the entire quantitative volumetric group, the mean volumetric error of FSL-FIRST was 2.74% (0.360 ml): among only corrected cases, the mean volumetric error was 6.79% (0.894 ml). The average percent volumetric error associated with seven error types, two anatomical variants, and motions artifacts are reported. Additional analyses showed that the presence of motion artifacts or anatomical variations significantly increased the probability of error (χ2 = 18.14, p < .01 and χ2 = 64.89, p < .001, respectively). Finally, thalamus volume error was negatively associated with degree of atrophy, such that smaller thalami were systematically overestimated (r = -.28, p < .001).

Conclusions: In PwMS, FSL-FIRST thalamic segmentation miscalculates thalamic volumetry in a predictable fashion, and may be biased to overestimate highly atrophic thalami. As such, it is recommended that segmentations be reviewed and corrected manually when appropriate for specific studies.

Keywords: atrophy; errors; segmentation; thalamus; volumetry.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Atrophy / diagnostic imaging
  • Atrophy / pathology
  • Humans
  • Magnetic Resonance Imaging / methods
  • Multiple Sclerosis* / diagnostic imaging
  • Multiple Sclerosis* / pathology
  • Thalamus / diagnostic imaging
  • Thalamus / pathology