Knee Cartilage Segmentation Using Diffusion-Weighted MRI
This work addresses the need for efficient and reliable cartilage segmentation in early osteoarthritis diagnosis, though it is incremental as it applies an existing method (U-Net) to a specific medical imaging task.
The paper tackles the problem of automating knee cartilage segmentation from diffusion-weighted MRI, which is currently done manually and is time-consuming and error-prone, and demonstrates that their modified U-Net ensemble model outperforms human expert segmentation in accuracy for Patellar and Tibial cartilage using dice scores.
The integrity of articular cartilage is a crucial aspect in the early diagnosis of osteoarthritis (OA). Many novel MRI techniques have the potential to assess compositional changes of the cartilage extracellular matrix. Among these techniques, diffusion tensor imaging (DTI) of cartilage provides a simultaneous assessment of the two principal components of the solid matrix: collagen structure and proteoglycan concentration. DTI, as for any other compositional MRI technique, require a human expert to perform segmentation manually. The manual segmentation is error-prone and time-consuming ($\sim$ few hours per subject). We use an ensemble of modified U-Nets to automate this segmentation task. We benchmark our model against a human expert test-retest segmentation and conclude that our model is superior for Patellar and Tibial cartilage using dice score as the comparison metric. In the end, we do a perturbation analysis to understand the sensitivity of our model to the different components of our input. We also provide confidence maps for the predictions so that radiologists can tweak the model predictions as required. The model has been deployed in practice. In conclusion, cartilage segmentation on DW-MRI images with modified U-Nets achieves accuracy that outperforms the human segmenter. Code is available at https://github.com/aakashrkaku/knee-cartilage-segmentation