Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bones of the Lower Limb: Tibia and Fibula01:10

Bones of the Lower Limb: Tibia and Fibula

3.2K
The tibia is the main weight-bearing bone of the lower leg. It is larger than the fibula with which it is paired. The tibia is also the second longest bone in the body and is located right below the skin. The proximal end of the tibia forms the medial and the lateral condyle, which articulates with the condyles of the femur to form the knee joint. Between the articulating surfaces is the irregular elevated area known as the intercondylar eminence that serves as the inferior attachment point for...
3.2K
Bones of the Lower Limb: Femur and Patella01:16

Bones of the Lower Limb: Femur and Patella

2.4K
The femur is the body's longest and strongest bone spanning the thigh region. Its head articulates with the acetabulum of the hip bone to form the hip joint. A minor indentation on the medial side of the femoral head, called the fovea capitis, serves as the site of attachment for the ligament of the head of the femur. This weak ligament spans the femur and acetabulum and supports the hip joint. The narrowed region below the head is the neck of the femur. The inclination angle between the...
2.4K

You might also read

Related Articles

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

Sort by
Same author

The ageing meniscus: Pathophysiology, epidemiology and management.

Journal of experimental orthopaedics·2026
Same author

Data-Driven Characterization of Knee Structures Using Non-Negative Matrix Factorization of 3D Multi-Echo UTE MRI.

NMR in biomedicine·2026
Same author

ICRS-FIFA-Aspetar consensus on the management of knee cartilage injuries in football players: part 2-appropriateness of specific surgical procedures to address articular cartilage lesions in different clinical scenarios using the RAND/UCLA appropriateness method.

British journal of sports medicine·2026
Same author

Return to sport after meniscectomy, meniscal repair, and meniscal allograft transplantation for meniscal lesions in athletes: A systematic review and meta-analysis.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA·2026
Same author

Five-Year Follow-up of a Multicenter Randomized Controlled Trial Comparing an Aragonite-Based Scaffold With Microfracture and Debridement for Chondral and Osteochondral Knee Lesions.

The American journal of sports medicine·2026
Same author

Can we Predict the Outcomes of Arthroscopic Partial Meniscectomy?

Current reviews in musculoskeletal medicine·2026

Related Experiment Video

Updated: Jun 27, 2025

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum
04:32

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Published on: March 19, 2017

7.5K

Automated Landmark Annotation for Morphometric Analysis of Distal Femur and Proximal Tibia.

Jonas Grammens1,2, Annemieke Van Haver3, Imelda Lumban-Gaol4

  • 1Antwerp Surgical Training, Anatomy and Research Centre (ASTARC), University of Antwerp, Wilrijk, 2610 Antwerp, Belgium.

Journal of Imaging
|April 26, 2024
PubMed
Summary

An automated method for knee bone analysis significantly reduces time and operator variability compared to manual landmarking. This innovation offers a faster, more reliable approach for orthopedic diagnostics and personalized treatments.

Keywords:
3D landmark analysisknee MRImorphometricsorthopedics

More Related Videos

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

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

Published on: July 2, 2021

3.0K
Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model
07:32

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

12.1K

Related Experiment Videos

Last Updated: Jun 27, 2025

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum
04:32

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Published on: March 19, 2017

7.5K
In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

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

Published on: July 2, 2021

3.0K
Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model
07:32

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

12.1K

Area of Science:

  • Orthopedic biomechanics
  • Medical imaging analysis
  • Computational anatomy

Background:

  • Manual anatomical landmarking for knee bone morphometrics is time-consuming and prone to significant operator variability.
  • This variability impacts the accuracy of diagnostics and personalized treatments in orthopedics, including implant sizing and risk assessment.

Purpose of the Study:

  • To develop and validate an automated method for anatomical landmark annotation on 3D knee bone models derived from MRI.
  • To compare the accuracy, reliability, and efficiency of the automated method against manual landmarking by multiple experts.

Main Methods:

  • Manual landmark annotation by three experts on 20 MRI-based 3D bone and cartilage models.
  • Development of an automated annotation technique using elastic deformation of a template shape and landmark optimization.
  • Comparison of automated results with expert-derived ground truth, assessing landmark position and morphometric measurements.

Main Results:

  • Automated landmark extraction showed an average difference of 2.05 mm compared to manual methods.
  • Automated morphometric measurements had an average difference of 0.78 mm from manual measurements.
  • 92% of automated landmarks and 95% of measurements were within acceptable tolerances (4 mm and 2 mm, respectively) of expert means.
  • Automated annotation reduced operator time from 18 minutes to 7 minutes of computing time, with high reliability (ICC 0.926-1).

Conclusions:

  • The automated landmark annotation method demonstrates high accuracy and reliability for knee bone morphometric analysis.
  • This automated approach offers a substantial improvement in efficiency, standardization, and operator independence over manual methods.
  • The developed algorithm facilitates faster, scalable, and more consistent orthopedic diagnostics and personalized treatment planning.