Quantitative analysis of lower extremity images typically require manual or semi-automated segmentation of regions of interest. This can be extremely time consuming. Here, we utilise DeepLearning and a database of previously segmented thigh and calf t1-weighted images to automatically segment the images into different tissue types and various muscle groups. Dice scores greater than 0.85 were achieved on average across the classes with as few as 40 training images (3D). In addition, we demonstrate a method for training the model with partially labelled images, enabling access to potentially much larger training datasets.