  Segmentation of magnetic resonance (MR) images is a fundamental step in many
medical imaging-based applications. The recent implementation of deep
convolutional neural networks (CNNs) in image processing has been shown to have
significant impacts on medical image segmentation. Network training of
segmentation CNNs typically requires images and paired annotation data
representing pixel-wise tissue labels referred to as masks. However, the
supervised training of highly efficient CNNs with deeper structure and more
network parameters requires a large number of training images and paired tissue
masks. Thus, there is great need to develop a generalized CNN-based
segmentation method which would be applicable for a wide variety of MR image
datasets with different tissue contrasts. The purpose of this study was to
develop and evaluate a generalized CNN-based method for fully-automated
segmentation of different MR image datasets using a single set of annotated
training data. A technique called cycle-consistent generative adversarial
network (CycleGAN) is applied as the core of the proposed method to perform
image-to-image translation between MR image datasets with different tissue
contrasts. A joint segmentation network is incorporated into the adversarial
network to obtain additional segmentation functionality. The proposed method
was evaluated for segmenting bone and cartilage on two clinical knee MR image
datasets acquired at our institution using only a single set of annotated data
from a publicly available knee MR image dataset. The new technique may further
improve the applicability and efficiency of CNN-based segmentation of medical
images while eliminating the need for large amounts of annotated training data.
