Automatic Segmentation of Bone Marrow Lesions on MRI Using a Deep Learning Method

Author:

Ponnusamy Raj1ORCID,Zhang Ming2,Wang Yue1,Sun Xinyue3,Chowdhury Mohammad1,Driban Jeffrey B.4,McAlindon Timothy5,Shan Juan1ORCID

Affiliation:

1. Department of Computer Science, Seidenberg School of CSIS, Pace University, New York City, NY 10038, USA

2. Department of Computer Science, Boston University, Boston, MA 02215, USA

3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

4. Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, MA 01655, USA

5. Division of Rheumatology, Allergy, and Immunology, Tufts Medical Center, Boston, MA 02111, USA

Abstract

Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the BML may occur. It is also time-consuming to delineate BMLs manually. In this paper, we proposed a fully automatic segmentation method for BMLs without requiring human intervention. The model takes intermediate weighted fat-suppressed (IWFS) magnetic resonance (MR) images as input, and the output BML masks are evaluated using both regular 2D Dice similarity coefficient (DSC) of the slice-level area metric and 3D DSC of the subject-level volume metric. On a dataset with 300 subjects, each subject has a sequence of 36 IWFS MR images approximately. We randomly separated the dataset into training, validation, and testing sets with a 70%/15%/15% split at the subject level. Since not every subject or image has a BML, we excluded the images without a BML in each subset. The ground truth of the BML was labeled by trained medical staff using a semi-automatic tool. Compared with the ground truth, the proposed segmentation method achieved a Pearson’s correlation coefficient of 0.98 between the manually measured volumes and automatically segmented volumes, a 2D DSC of 0.68, and a 3D DSC of 0.60 on the testing set. Although the DSC result is not high, the high correlation of 0.98 indicates that the automatically measured BML volume is strongly correlated with the manually measured BML volume, which shows the potential to use the proposed method as an automatic measurement tool for the BML biomarker to facilitate the assessment of knee OA progression.

Publisher

MDPI AG

Reference38 articles.

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2. Centers for Disease Control and Prevention (2023, February 17). National Statistics, Available online: https://www.cdc.gov/arthritis/data_statistics/national-statistics.html.

3. Updated projected prevalence of self-reported doctor-diagnosed arthritis and arthritis-attributable activity limitation among US adults, 2015–2040;Hootman;Arthritis Rheumatol.,2016

4. Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score);Hunter;Osteoarthr. Cartil.,2011

5. Evaluation of bone marrow lesion volume as a knee osteoarthritis biomarker-longitudinal relationships with pain and structural changes: Data from the Osteoarthritis Initiative;Driban;Arthritis Res. Ther.,2013

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