Congratulations to Dr Joshua Kaggie, Dr Stephen McDonnell and the team at the University of Cambridge on the acceptance of their article for publication in Osteoarthritis Imaging.

Their article, “Segmentation of Knee MRI Data with Convolutional Neural Networks for Semi-Automated Three-Dimensional Surface-Based Analysis of Cartilage Morphology and Composition is currently in press. The objective of this work is to assess automatic segmentations for surface-based analysis of cartilage morphology and composition on knee magnetic resonance (MR) images. You can read the pre-proof online here.

Abstract:

Objective

To assess automatic segmentations for surface-based analysis of cartilage morphology and composition on knee magnetic resonance (MR) images.

Methods

2D and 3D U-Nets were trained on double echo steady state (DESS) images from the publicly available Osteoarthritis Initiative (OAI) dataset with femoral and tibial bone and cartilage segmentations provided by the Zuse Institute Berlin (ZIB). The U-Nets were used to perform automatic segmentation of femoral and tibial bone-cartilage structures (bone and cartilage segmentations combined into one structure) from the DESS images. T2-weighted images from the OAI dataset were registered to the DESS images and used for T2 map calculation. Using the 3D cartilage surface mapping (3D-CaSM) method, surface-based analysis of cartilage morphology (thickness) and composition (T2) was performed using both manual and network-generated segmentations from OAI ZIB testing images. Bland-Altman analyses were performed to evaluate the accuracy of the extracted cartilage thickness and T2 measurements from both U-Nets compared to manual segmentations.

Results

Bland-Altman analysis showed a mean bias [95% limits of agreement] for femoral and tibial cartilage thickness measurements ranging between -0.12 to 0.33 [-0.28, 0.96] mm with 2D U-Net and 0.07 to 0.14 [-0.14, 0.39] mm with 3D U-Net. For T2, the mean bias [95% limits of agreement] ranged between -0.16 to 1.32 [-4.71, 4.83] ms with 2D U-Net and -0.05 to 0.46 [-2.47, 3.39] ms with 3D U-Net.

Conclusions

While both 2D and 3D U-Nets exemplified the time-efficiency benefit of using deep learning methods for generating the required segmentations, segmentations from 3D U-Nets demonstrated higher accuracy in the extracted thickness and T2 features using 3D-CaSM compared to the segmentations from 2D U-Nets.

 

The Osteoarthritis Imaging journal disseminates current knowledge and information on imaging in osteoarthritis and deals with advances in the methods and application of medical imaging in the context of basic science, clinical osteoarthritis research, its clinical application and clinical trials.