Model-Based Analysis of Biomedical Images Application to Cartilage Thickness Measurement in Drug Development

University of Manchester AstraZeneca Pharmaceuticals iMorphics Ltd Medical Image and Signal IRC

3D MR Images of the Knee

Cartilage Loss in Osteoarthritis Degeneration of cartilage in disease Analysis using cartilage thickness maps Cartilage thickness map

Aims of Image Analysis Extract structures of interest (automatically) –segmentation of bone, cartilage … –requires anatomical knowledge Construct cartilage thickness maps –stable measurement frame for each individual –common reference frame for population analysis

Model-Based Segmentation Parameterised statistical models of anatomy Model of femurUsing the model to segment the femur

Learning the Model Statistical analysis of corresponding point-sets Correspondence by optimisation Minimising model complexity

Obtaining a Thickness Map Bone provides stable measurement frame Cartilage thickness mapped to bone surface

Population Analysis Model correspondences provide common frame Population results displayed on average femur Average thickness map

Summary Quantitative analysis of 3D medical images Statistical models of anatomy –optimisation with respect to correspondence –model-based segmentation Cartilage thickness maps relative to bone Population analysis in average bone frame