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Computer-assisted medical image analysis plays a vital role in supporting radiologists in making clinical diagnoses. Medical imaging is the most important tool that is used for diagnosis of diseases and injuries. Radiologists use images to diagnose and monitor patients' conditions, identify abnormalities, and determine treatments. They are now working with AI to help them analyze these images more efficiently. Computer vision is a branch of computer science that deals with how to extract information from images or videos. It includes methods for acquiring, processing, analyzing and understanding images, as well as the development of systems for interpreting what the images show. There are many fields that can benefit from computer vision. One such field is medical imaging analysis where CNNs have been found to be a good choice.

Computer vision is the science of analyzing images and videos to extract information. Computer Vision is a fast-growing field with many applications in different industries. Researchers are working hard to develop new methods for image segmentation, which is the process of dividing an image into different parts or regions. This can be done automatically or by hand. The goal of computer vision algorithms is to extract useful information from images, such as object locations and orientations, object shape, texture, color and so on.

In this research, we present a deep learning method for 3D MRI segmentation. We first use the depth map to extract the foreground from the background of an MR image. Then we train a convolutional neural network (CNN) to classify each voxel into one of four classes: bone, fat, muscle or background.