Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning

• URL: http://arxiv.org/abs/2012.12406v1
• Date: Tue, 22 Dec 2020 23:08:41 GMT
• Title: Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning
• Authors: Kevin A. Thomas (1), Dominik Krzemi\'nski (2), {\L}ukasz Kidzi\'nski (3), Rohan Paul (1), Elka B. Rubin (4), Eni Halilaj (5), Marianne S. Black (4) Akshay Chaudhari (1,4), Garry E. Gold (3,4,6), Scott L. Delp (3,6,7) ((1) Department of Biomedical Data Science, Stanford University, California, USA (2) Cardiff University Brain Research Imaging Centre, Cardiff University, United Kingdom (3) Department of Biomedical Engineering, Stanford University, California, USA (4) Department of Radiology, Stanford University, California, USA (5) Department of Mechanical Engineering, Carnegie Mellon University, Pennsylvania, USA (6) Department of Orthopaedic Surgery, Stanford University, California, USA (7) Department of Mechanical Engineering, Stanford University, California, USA)
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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