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

Raj Ponnusamy1, Ming Zhang2, Yue Wang1

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

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Summary
This summary is machine-generated.

This study presents an automated method for segmenting bone marrow lesions (BMLs) in knee osteoarthritis (KOA) using MRI. The approach accurately measures BML volume, correlating highly with manual measurements, aiding KOA assessment.

Keywords:
bone marrow lesionscomputer-aided diagnosisdeep learningknee osteoarthritissegmentation

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Area of Science:

  • Radiology
  • Medical Imaging
  • Osteoarthritis Research

Background:

  • Bone marrow lesion (BML) volume is a key biomarker for knee osteoarthritis (KOA), linked to cartilage damage and pain.
  • Manual segmentation of BMLs is challenging due to their small size, low contrast, and varied locations, making it time-consuming and difficult.
  • Accurate and efficient quantification of BMLs is crucial for monitoring KOA progression.

Purpose of the Study:

  • To develop and validate a fully automatic method for segmenting bone marrow lesions (BMLs) in knee osteoarthritis (KOA) using magnetic resonance (MR) imaging.
  • To assess the performance of the automated method by comparing its BML volume measurements against manually derived ground truth.
  • To evaluate the potential of the automated method as a tool for facilitating KOA assessment and tracking disease progression.

Main Methods:

  • A fully automatic segmentation model was developed, utilizing intermediate weighted fat-suppressed (IWFS) MR images as input.
  • The model was trained and validated on a dataset of 300 subjects, with a 70%/15%/15% split for training, validation, and testing, respectively.
  • Segmentation accuracy was evaluated using 2D Dice Similarity Coefficient (DSC) for slice-level area and 3D DSC for subject-level volume, alongside Pearson's correlation coefficient for volume measurement agreement.

Main Results:

  • The automated segmentation method achieved a high Pearson's correlation coefficient of 0.98 between automatically measured and manually measured BML volumes on the testing set.
  • The method yielded a 2D DSC of 0.68 and a 3D DSC of 0.60, indicating moderate agreement in segmentation accuracy.
  • Despite moderate DSC scores, the strong correlation in volume measurement highlights the method's utility for quantitative BML assessment.

Conclusions:

  • The proposed fully automatic segmentation method demonstrates strong potential for accurately measuring bone marrow lesion (BML) volume in knee osteoarthritis (KOA).
  • The high correlation between automated and manual volume measurements suggests the method can serve as an efficient tool for assessing KOA progression.
  • This automated approach can significantly reduce the time and effort required for BML quantification, thereby facilitating clinical assessment and research in KOA.