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Related Experiment Video

Updated: Jul 19, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Advancing deep learning based knee cartilage segmentation in MRI: Innovations, challenges and applications.

Sheheryar Khan¹, Muhammad Ammar Khawer¹, Junru Zhong²

¹Division of Science Engineering, and Health Studies (SEHS), School of Professional Education and Executive Development, The Hong Kong Polytechnic University, Hong Kong.

Osteoarthritis and Cartilage Open

|December 9, 2025

Summary

Deep learning (DL) significantly improves knee cartilage segmentation in MRI, offering automated solutions for osteoarthritis assessment. These advanced methods enhance accuracy and efficiency compared to manual approaches.

Keywords:

Cartilage segmentation Cartilage thickness mapping Deep learning Foundation models Knee MRI Osteoarthritis

Related Experiment Videos

Last Updated: Jul 19, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Orthopedics

Background:

Manual knee cartilage segmentation in MRI is time-consuming and prone to variability.
Osteoarthritis (OA) assessment using MRI requires consistent and reproducible cartilage quantification.
Deep learning (DL) offers scalable, automated solutions to overcome limitations of manual segmentation.

Purpose of the Study:

To review state-of-the-art DL-based approaches for knee cartilage segmentation.
To evaluate various DL architectures, techniques, and their adaptability to diverse MRI datasets and protocols.
To highlight challenges and solutions in DL-based knee cartilage segmentation for OA assessment.

Main Methods:

Review of recent DL advancements for knee cartilage segmentation.
Focus on evaluation of different DL architectures and techniques.
Discussion of adaptability to diverse datasets and imaging protocols.

Main Results:

DL methods show substantial improvements in segmentation accuracy and efficiency over conventional methods for knee MRI.
Key challenges like data scarcity, domain shifts, and imaging variability are addressed.
Clinical applications demonstrate potential for cartilage thickness mapping and OA assessment.

Conclusions:

DL-based segmentation is advancing musculoskeletal imaging with reliable, automated solutions.
Despite challenges, advancements in semi-supervised learning, domain adaptation, and foundation models enhance robustness.
These advancements expand the clinical applicability of automated knee cartilage segmentation for OA management.