Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Knee Joint

Knee Joint

The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris group...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment.

Radiology. Artificial intelligence·2026

Same author

"Enhancing Patient Understanding of Radiology Reports Through LLM-Generated Summaries, Clickable Terms, and AI Videos".

Journal of the American College of Radiology : JACR·2026

Same author

Review of breast arterial calcifications for cardiovascular disease risk prediction.

NPJ cardiovascular health·2026

Same author

CLEAR-AI: confounder-aware learning for equitable and accurate reasoning in AI for diagnosis.

Journal of medical imaging (Bellingham, Wash.)·2026

Same author

Machine Learning Models of Phase Contrast Images Predict Efficiency of Human Pluripotent Stem Cell Differentiation to Cardiomyocytes.

Biotechnology and bioengineering·2026

Same author

Risk Stratification for Breast Cancer Screening: <i>AJR</i> Expert Panel Narrative Review.

AJR. American journal of roentgenology·2026

Same journal

How is Bias Learned in Medical Image Analysis Models? An Exploration of the Encoding of Demographic Information in Deep Learning Models Trained to Detect Abnormalities on Chest X-Rays.

Journal of imaging informatics in medicine·2026

Same journal

AI-Based Opportunistic CT Risk Assessment Using TotalSegmentator in Osteoporotic Vertebral Fractures.

Journal of imaging informatics in medicine·2026

Same journal

A Computationally Efficient and Improved Brain Tumor Recognition System by MRI-Segmentation Integrated Classification Network.

Journal of imaging informatics in medicine·2026

Same journal

Ultrasound Domain Adaptation for Robust Kidney Segmentation via Spectral-Similarity-Guided Translation.

Journal of imaging informatics in medicine·2026

Same journal

Dynamic Fuzzy-Gaussian Modeling (DynFGM): A Kurtosis-Adaptive Unsupervised Framework for Automated Adipose Tissue Segmentation in Abdominal MRI.

Journal of imaging informatics in medicine·2026

Same journal

Computerized Classification Method for Glioma Molecular Subtypes on Brain MR Images Using SAM-Med3D with Low-Rank Adaptation.

Journal of imaging informatics in medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 13, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

An Intensity-Based Cropping Approach for Fast, Interpretable, and Robust Localization of the Knee Joint in

Mohammadreza Chavoshi¹, Hari Trivedi¹, Janice Newsome¹

¹Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road NE, Atlanta, GA, 30322, USA.

Journal of Imaging Informatics in Medicine

|May 11, 2026

Summary

This summary is machine-generated.

An intensity-based cropping algorithm for knee imaging is a fast, annotation-free alternative to deep learning methods. It ensures robust model training by focusing on relevant anatomy, improving diagnostic consistency.

Keywords:

Anatomical landmark Artificial intelligence Deep learning Image analysis Knee Radiography

More Related Videos

Non-Invasive Compression-Induced Anterior Cruciate Ligament (ACL) Injury and In Vivo Imaging of Protease Activity in Mice

Non-Invasive Compression-Induced Anterior Cruciate Ligament (ACL) Injury and In Vivo Imaging of Protease Activity in Mice

Published on: September 29, 2023

Related Experiment Videos

Last Updated: May 13, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

Non-Invasive Compression-Induced Anterior Cruciate Ligament (ACL) Injury and In Vivo Imaging of Protease Activity in Mice

Non-Invasive Compression-Induced Anterior Cruciate Ligament (ACL) Injury and In Vivo Imaging of Protease Activity in Mice

Published on: September 29, 2023

Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Computational Anatomy

Background:

Effective image preprocessing is crucial for robust deep learning (DL) models, preventing shortcut learning on spurious features.
Knee joint localization is essential for reliable pathology assessment, isolating clinically meaningful anatomy.
Accurate region of interest selection enhances DL model training quality and diagnostic performance.

Purpose of the Study:

Introduce a fully automated, intensity-based cropping algorithm for knee joint localization.
Compare its performance against Support Vector Machine (SVM) and DL-based methods.
Evaluate its clinical utility, computational efficiency, and distributional consistency.

Main Methods:

Developed a deterministic, intensity-based algorithm using anatomical landmarks without annotations or training.
Evaluated cropping performance on OAI and MRKR datasets using Intersection over Union (IoU), Dice, and mAP@0.5.
Assessed computational efficiency and trained ConvNeXt models for osteoarthritis prediction using crops from each method.

Main Results:

DL-based method achieved highest localization accuracy (IoU: 0.737), followed by intensity-based (IoU: 0.692).
Intensity-based method was significantly faster (0.047 s/image) and processed 100% of images.
Models trained on intensity-based crops showed stable performance and more homogeneous feature distributions.

Conclusions:

The proposed intensity-based algorithm is a robust, annotation-free alternative to supervised methods.
Its deterministic nature and minimal computational cost support large-scale research and clinical deployment.
This method enhances diagnostic reliability by reducing artifacts and spurious features in medical image analysis.