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Growth of Cartilage and Bone Tissue

Growth of Cartilage and Bone Tissue

Chondrocytes form a temporary cartilaginous model by dividing and secreting a thick gel-like extracellular matrix. Once the chondrocytes undergo programmed cell death, osteoblasts enter the site of the cartilaginous model. The process of replacing the temporary cartilaginous model with bone in an ordered manner is called endochondral ossification. In endochondral ossification, not all of the cartilage is replaced by bone tissue. Some cartilage that performs a protective and supportive function...

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Towards Automatic Cartilage Quantification in Clinical Trials - Continuing from the 2019 IWOAI Knee Segmentation

Erik B Dam¹, Arjun D Desai², Cem M Deniz³

¹University of Copenhagen, Copenhagen, Denmark.

Osteoarthritis Imaging

|July 22, 2024

Summary

Deep learning (DL) segmentation methods show promise for quantifying knee cartilage loss in clinical trials. While effective for tibial compartments, further refinement is needed for femoral compartments to match manual accuracy.

Keywords:

MRI cartilage clinical trial deep learning knee

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Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Osteoarthritis research

Background:

Accurate quantification of knee cartilage loss is crucial for longitudinal clinical trials in osteoarthritis.
Deep learning (DL) segmentation methods offer potential for automated analysis of knee MRIs.
Evaluating the performance of DL methods against established manual techniques is essential.

Purpose of the Study:

To assess the suitability of DL segmentation methods from the IWOAI 2019 challenge for quantifying cartilage loss in clinical trials.
To compare the sensitivity of DL methods to manual cartilage volume scoring using the standardized response mean (SRM).

Main Methods:

Utilized 556 subjects from the Osteoarthritis Initiative with baseline and 1-year knee MRI scans.
Six DL teams applied their methods to segment 1130 anonymized knee MRIs.
Extracted tibial and femoral compartments, calculating SRM to measure cartilage loss sensitivity.

Main Results:

Several DL methods achieved SRMs comparable to the gold standard manual method for tibial compartments.
The highest DL SRM for the lateral tibial compartment was 0.38, closely matching the gold standard (0.34).
Gold standard manual methods demonstrated higher SRMs (0.31/0.30) than DL methods (0.2) for femoral compartments.

Conclusions:

State-of-the-art DL segmentation methods show potential for use in standardized, longitudinal, single-scanner clinical trials.
The performance of DL methods in femoral compartments may be limited by post-processing sub-compartment extraction techniques.
Further optimization of DL algorithms and processing steps is warranted for robust cartilage loss quantification.