T. Cootes

R. Aspden, J. Gregory, N. Harvey, 2022, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

Automatic Segmentation of Hip Osteophytes in DXA Scans Using U-Nets

J. Gregory, J. Tobias, N. Harvey, 2022, MICCAI.

An Introduction to Active Shape Models

Tim Cootes, T. Cootes, 2000 .

Indexed distribution analysis for improved significance testing of spatially heterogeneous parameter maps: Application to dynamic contrast‐enhanced MRI biomarkers

Geoff J M Parker, John C Waterton, Chris J Rose, 2013, Magnetic resonance in medicine.

Increasing shape modelling accuracy by adjusting for subject positioning: An application to the analysis of radiographic proximal femur symmetry using data from the Osteoarthritis Initiative☆

T. Cootes, G. Wallis, C. Lindner, 2014, Bone.

Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting.

Claudia Lindner, Tim F Cootes, Paul A Bromiley, 2015, IEEE transactions on pattern analysis and machine intelligence.

Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting

Paul A. Bromiley, Claudia Lindner, Tim F. Cootes, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Predicting the mechanical hip–knee–ankle angle accurately from standard knee radiographs: a cross-validation experiment in 100 patients

S. Y. Ahmadi Brooghani, H. Weinans, P. D. de Jong, 2020, Acta orthopaedica.

Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms

Ching-Wei Wang, Claudia Lindner, Cheng-Ta Huang, 2016, Scientific Reports.

Defect Detection and Classification by Training a Generic Convolutional Neural Network Encoder

Xinghui Dong, Christopher J. Taylor, Tim F. Cootes, 2020, IEEE Transactions on Signal Processing.

Osteophyte size and location on hip DXA scans are associated with hip pain: Findings from a cross sectional study in UK Biobank

R. Aspden, G. Davey Smith, J. Gregory, 2021, medRxiv.

Development of a fully automatic shape model matching (FASMM) system to derive statistical shape models from radiographs: application to the accurate capture and global representation of proximal femur shape.

C Lindner, S Thiagarajah, J M Wilkinson, 2013, Osteoarthritis and cartilage.

Facial Behaviour Analysis in Parkinson's Disease

Monty Silverdale, Samuel Couth, Ellen Poliakoff, 2016, MIAR.

Automatic Inspection of Aerospace Welds Using X-Ray Images

Xinghui Dong, Tim F. Cootes, Chris J. Taylor, 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

Small Defect Detection Using Convolutional Neural Network Features and Random Forests

Xinghui Dong, Tim F. Cootes, Chris J. Taylor, 2018, ECCV Workshops.

The identification of distinct protective and susceptibility mechanisms for hip osteoarthritis: findings from a genome-wide association study meta-analysis of minimum joint space width and Mendelian randomisation cluster analyses

J. V. van Meurs, E. Zeggini, R. Aspden, 2023, EBioMedicine.

Hip joint space width is causally related to hip osteoarthritis risk via distinct protective and susceptibility mechanisms: findings from a genome-wide association study meta-analysis

E. Zeggini, R. Aspden, J. Kemp, 2023, medRxiv.

Accurate markerless respiratory tracking for gated whole body PET using the Microsoft Kinect

Jon Howard, Philip J. Noonan, Tim F. Cootes, 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

ACETABULAR DYSPLASIA AND CAM MORPHOLOGY ARE FEATURES OF SEVERE HIP OSTEOARTHRITIS: FINDINGS FROM A CROSS-SECTIONAL STUDY IN UK BIOBANK

R. Aspden, J. Gregory, F. Saunders, 2022, Osteoarthritis and Cartilage.

Hip geometric parameters are associated with radiographic and clinical hip osteoarthritis: findings from a cross-sectional study in UK Biobank

R. Aspden, J. Gregory, J. Tobias, 2023, medRxiv.

Synovial volume vs synovial measurements from dynamic contrast enhanced MRI as measures of response in osteoarthritis

C. Hutchinson, T. Cootes, E. Marjanovic, 2020 .