Computer Vision in Knee MRI Segmentation to the Human Tibia Bone

This article presents the development process of a Machine Learning model to gain understanding from Digital Magnetic Resonance Images (MRI) of the Human Knee and label the corresponding pixels of the image to the Tibia bone, using a Deep Learning network and image segmentation. Deep Convolutional networks have outperformed the state of the art in many visual recognition tasks, the image semantic segmentation challenge consists in classifying each pixel of an image into an instance corresponding to an object or a part of the image. The data set used, consisting of a total of 90 cases of the Human knee medical images, also known as Magnetic resonance Imaging MRI. Each case consists of a set of 160 medical images of the knee in format type Digital Imaging and Communications in Medicine or DICOM. In order to extract the area of interest in each DICOM image, the Tibia bone was labeled with a software called BML BaseLine, this software is used to mark the bounds of the bone on each DICOM image for each case.