Blog 4: End of Year Report

Our long term goal is to have a fully trained machine learning model that is able to detect the cartilage in knee MRI images of patients in order to help facilitate the diagnosis of Osteoarthritis. While we have not yet achieved this goal, one big part in working towards this goal is to create a model that can actually detect the three bones present in each MRI image: the Femur, Tibia, and Patella. As stated in previous blog posts, we have been working on training a model for each bone, one at a time. In order to train a model to detect where the bone is in the MRI image we need to feed the images with a corresponding bone mask image that is manually generated with software. We had cases labeled for the Femur when I started working on the project, so I had to manually label the cases for the Tibia model so that they could be fed to the model. The way the model works is that when it is fully trained with the manually labeled cases, any new unlabeled cases that are fed into the model could have an image mask generated by the model without the need to manually label it.

I worked closely with my mentor, Dr. Shan, throughout both semesters and last year, meting weekly to make sure that all goals are being met and making sure everything was getting completed efficiently. At this point in the semester we were able to get great results in our trained models. We have a working Femur model that runs at around 97.78% accuracy on any new images piped into the model. The Tibia model runs at 96.85% accuracy on images. Since our next goal is to have a combined model with all three bones, it was necessary to train a model with just the Tibia and Femur bone images present in the mask since the Patella model isn’t completely labeled and trained yet. I needed to write code to combine the image masks for the Tibia and the Patella patient cases that were manually labeled. I was then able to feed these combined masks with the original image to the model and create a combined Tibia and Femur model. This model had an accuracy 97.16% on any new images fed into the model which is high for this kind of model. This means that we are ready to combine the Femur and Tibia with the Patella once those are fully labeled and a model has been trained on those cases. We can use the combined model that has been trained to detect the bone in MRI slices to then detect cartilage between the bones much easier, which is of course our main objective. Overall, this year we were able to achieve a lot of results for this project and Dr. Shan and I are planning on continuing to work on the project next semester.

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