Blog Post #3 Successful Training of Tibia and Femur Models

We are making great progress in meeting our goal in creating a successful fully trained model with excellent accuracy. As stated in the previous blog post, we have a trained model on the Femur dataset which has a very high accuracy, around 97%. We had originally planned on training a Tibia model without using all the cases in the training division, because they had not been properly corrected, and our tests with 30% of the training dataset proved to have a good outcome on the Femur data which as stated previously already had a properly trained model. Since the tests with a condensed dataset on the Femur had positive outcomes, we decided to move onto tests on the Tibia. We trained the Tibia model using only 35 training cases, 15 validation and 15 testing cases. The model obtained some I interesting results.

This model trained surprisingly had an accuracy of around 95.97% accuracy after it was trained, which was quite a surprise to both Dr. Shan and myself considering the amount of the training data used. The model makes predictions on a group of testing cases in order to see if the model is well trained, and for this model the predictions were wildly inaccurate and had many areas of the bone in the predicated image mask that had incorrect image segmentation. So we decided that it was best if I just manually corrected the entire dataset so that we can train the model with our original dataset of 70 training cases, 15 validation, and 15 testing cases. This model had an accuracy of 96.85% which is as expected and similar to the Femur tests. The predictions in this case were also accurate and had very little to no incorrectly segmented areas of the Tibia bone.

Now all that is left is to complete labeling of the Patella data and train that model, which is currently being done by another student. Currently our goal is to combine the Tibia and Femur models into one and to see what accuracy can be achieved on this model. This involves coding a script to properly combine the data which can then be used in the code to create and train the model.

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