From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research
Knee osteoarthritis is a major diarthrodial joint disorder with profound global socioeconomic impact. Diagnostic imaging using magnetic resonance image can produce morphometric biomarkers to investigate the epidemiology of knee osteoarthritis in clinical trials, which is critical to attain early det...
Saved in:
Main Authors: | Gan, Hong Seng, Ramlee, Muhammad Hanif, Abdul Wahab, Asnida, Lee, Yeng Seng, Shimizu, Akinobu |
---|---|
Format: | Article |
Published: |
Springer Science and Business Media B.V.
2020
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/90433/ http://dx.doi.org/10.1007/s10462-020-09924-4 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Automated knee bone segmentation and visualisation using mask RCNN and marching cube: Data from the osteoarthritis initiative
by: Patekar, Rahul, et al.
Published: (2022) -
Multilabel graph based approach for knee cartilage segmentation: Data from the osteoarthritis initiative
by: Gan, H. S., et al.
Published: (2015) -
Gait characteristics and pain assessment of symptomatic knee osteoarthritis
by: Abdul Halim, Husnir Nasyuha, et al.
Published: (2021) -
K-means clustering in knee cartilage classification: Data from the OAI
by: Sia, Joyce Sin Yin, et al.
Published: (2020) -
Finite element modelling and simulation for lower limb of human bone: A review
by: Ramlee, Muhammad Hanif
Published: (2020)