  One of the most common tasks in medical imaging is semantic segmentation.
Achieving this segmentation automatically has been an active area of research,
but the task has been proven very challenging due to the large variation of
anatomy across different patients. However, recent advances in deep learning
have made it possible to significantly improve the performance of image
recognition and semantic segmentation methods in the field of computer vision.
Due to the data driven approaches of hierarchical feature learning in deep
learning frameworks, these advances can be translated to medical images without
much difficulty. Several variations of deep convolutional neural networks have
been successfully applied to medical images. Especially fully convolutional
architectures have been proven efficient for segmentation of 3D medical images.
In this article, we describe how to build a 3D fully convolutional network
(FCN) that can process 3D images in order to produce automatic semantic
segmentations. The model is trained and evaluated on a clinical computed
tomography (CT) dataset and shows state-of-the-art performance in multi-organ
segmentation.
