  Objective: We evaluate a fully-automated femoral cartilage segmentation model
for measuring T2 relaxation values and longitudinal changes using multi-echo
spin echo (MESE) MRI. We have open sourced this model and corresponding
segmentations. Methods: We trained a neural network to segment femoral
cartilage from MESE MRIs. Cartilage was divided into 12 subregions along
medial-lateral, superficial-deep, and anterior-central-posterior boundaries.
Subregional T2 values and four-year changes were calculated using a
musculoskeletal radiologist's segmentations (Reader 1) and the model's
segmentations. These were compared using 28 held out images. A subset of 14
images were also evaluated by a second expert (Reader 2) for comparison.
Results: Model segmentations agreed with Reader 1 segmentations with a Dice
score of 0.85 +/- 0.03. The model's estimated T2 values for individual
subregions agreed with those of Reader 1 with an average Spearman correlation
of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated
four-year change in T2 for individual regions agreed with Reader 1 with an
average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with
Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice
score (0.85 vs 0.75) and subregional T2 values. Conclusions: We present a fast,
fully-automated model for segmentation of MESE MRIs. Assessments of cartilage
health using its segmentations agree with those of an expert as closely as
experts agree with one another. This has the potential to accelerate
osteoarthritis research.
