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.

1 thought on “Blog Post #1 Bone Segmentation in 3D MRI Images”

Leave a Reply

Your email address will not be published. Required fields are marked *