Automated 3D muscle segmentation from MRI data using convolutional neural network
In this paper, we propose an automated segmentation algorithm for human leg muscles from 3D MRI data using a deep convolutional neural network (CNN). Using a generalized cylinder model, a 3D human leg muscle is represented by two smooth 2D images. The CNN predicts these two images from raw 3D voxels. For our base CNN, we use a pre-trained AlexNet that is coupled with a principle components analysis (PCA)-head. The AlexNet predicts a compressed vector, which is then back-projected by the PCA-head into two 2D images representing a 3D leg muscle boundary. This structured-output CNN architecture is fine-tuned in an end-to-end fashion. Our proposed CNN outperforms the conventional model-based approach, the active appearance model (AAM) image segmentation algorithm. The average Dice score between the ground truth segmentation and the obtained segmentation image is 0.85 using our CNN model, whereas the AAM yields a Dice score of 0.60.