Objective: We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically.
Design: 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 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 subregions 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: Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.
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http://dx.doi.org/10.1177/19476035211042406 | DOI Listing |
Skeletal Radiol
December 2024
Department of Radiology, Chobanian and Avedisian School of Medicine, Boston University, Boston, MA, USA.
Objective: To determine the accuracy of automatic Cobb angle measurements by deep learning (DL) on full spine radiographs.
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Cell Death Differ
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Department of Urology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
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November 2024
Department of breast surgery, The First Affiliated Hospital with Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China.
Int J Oncol
January 2025
Center for Chemoprevention and Cancer Drug Development, Department of Medicine, Hem-Onc Section, PC Stephenson Oklahoma Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
Following the publication of the above article, an interested reader drew to the authors' attention that certain of the in vitro image panels shown in Fig. 3B (featuring the effects of adding five different concentrations of omeprazole on acridine orange/ethidium bromide‑stained HCA‑7 cells) and Fig. 4 (showing western blotting experiments) on p.
View Article and Find Full Text PDFInt J Mol Med
February 2025
Department of Biomaterials Science, College of Natural Resources and Life Science/Life and Industry Convergence Research Institute, Pusan National University, Miryang 627-706, Republic of Korea.
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