  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
