Fast quantitative bone marrow lesion measurement on knee MRI for the assessment of osteoarthritis.
Osteoarthr Cartil Open
; 4(1): 100234, 2022 Mar.
Article
in En
| MEDLINE
| ID: mdl-36474467
ABSTRACT
Objective:
Knee osteoarthritis (KOA) is a prevalent disease with a high economic and social cost. Magnetic resonance imaging (MRI) can be used to visualize many KOA-related structures including bone marrow lesions (BMLs), which are associated with OA pain. Several semi-automated software methods have been developed to segment BMLs, using manual, labor-intensive methods, which can be costly for large clinical trials and other studies of KOA. The goal of our study was to develop and validate a more efficient method to quantify BML volume on knee MRI scans. Materials andmethods:
We have applied a deep learning approach using a patch-based convolutional neural network (CNN) which was trained using 673 MRI data sets and the segmented BML masks obtained from a trained reader. Given the location of a BML provided by the reader, the network performed a fully automated segmentation of the BML, removing the need for tedious manual delineation. Accuracy was quantified using the Pearson's correlation coefficient, by a comparison to a second expert reader, and using the Dice Similarity Score (DSC).Results:
The Pearson's R2 value was 0.94 and we found similar agreement when comparing two readers (R2 â= â0.85) and each reader versus the DL model (R2 â= â0.95 and R2 â= â0.81). The average DSC was 0.70.Conclusions:
We developed and validated a deep learning-based method to segment BMLs on knee MRI data sets. This has the potential to be a valuable tool for future large studies of KOA.
Full text:
1
Database:
MEDLINE
Type of study:
Guideline
/
Prognostic_studies
Language:
En
Journal:
Osteoarthr Cartil Open
Year:
2022
Type:
Article
Affiliation country:
United States