Blog Post #2

Our project has been successful so far and we have been able to make a lot of progress the last few months. As stated in the previous blog post, we first have to train the model to detect which parts of the MRI images are the Tibia, Femur, and Patella. The Femur bone dataset has been previously labeled and a model has been trained on that dataset already. This makes it easier because we already know the performance percentage and have a good idea of how well formed the model is. We are aiming to have a Tibia model that runs around 96% accuracy or better in tracing the bone. The past few months I and other students have been labeling the Tibia database which consists of 100 cases, the cases are divided into three subfolders. 70% of the data is dedicated to the training folders with the remaining 30% being dedicated to testing and validation folders. The model obviously uses the training data to train the model, and then checks how accurate it is with the validation data. Then the testing data can be used to make predictions and further test the model. There were some complications with the labeled Tibia dataset and some of the cases labeled by me and by other students needed to be relabeled as they weren’t as accurate as they could have been.

We have found that the model doesn’t need the full 70% of the training data to accurately train the model. Both Dr. Shan and I had agreed on a plan to correct the labeling on only a certain percentage of the data (around 30%) and then have the accurate model predict the rest of the cases that we need for the model. With this knowledge we are working on finishing up fitting the model for the Tibia. Once we have completed this model, we plan on moving to the Patella model. The dataset for the Patella is not yet completely labeled and as such we plan on potentially using a trained model to also predict the image masks on the unlabeled Patella data. Once we have all three models complete, we have to combine them into one finished model that has all three bones labeled. This could raise further questions because certain bones start appearing in the MRI image at different times, and we would need to alter the code in ways that could stop any potential errors. Next semester we plan on finishing up the models as quickly as possible so that we can work on getting the main model completed, and move on to detecting the cartilage, one of our final goals.

Blog Post #1 Bone Segmentation in 3D MRI Images

The title of our project is “Bone Segmentation in 3D MRI Images” and it is tied in with the detection and diagnosis of knee osteoarthritis. The purpose of the project is to make an easier diagnosis of the ailment by training a computer to read in 3D MRI images and differentiate between bones and cartilage. We plan on training a machine learning model, which can be tricky if not done in certain stages. To train the model to detect the cartilage, an important marker of osteoarthritis, and we first need to train the model to detect which parts of the MRI images are the bones that need to be singled out and not included in the final result. The computer needs to be able to know the Femur, Tibia, Patella and have the ability to trace these bones automatically. It is a semi-complicated process, but it is a great learning experience because machine learning is an important concept to understand.

I expect to learn a lot about machine learning and convolutional neural networks during this project because these topics go hand in hand in training a computer model. We will be using Tensorflow as our platform for machine learning because it is open source and has a lot of thorough documentation. The code for the U-Net model we are using is written in python which is a popular programming language that I have also started to learn because of the project. We will need to start by manually labeling the MRI database of patient cases that we will use in training the model. By manually training the image set we can then use a certain amount of the cases to train the model and then another set of cases will be used to test the model and validate that the computer came up with the proper results. We are hoping to achieve an accurate model that will be able to segment the bones on the MRI images automatically so that we can remove this segmentation afterwards in order to detect certain biomarkers, such as the cartilage.