  Automated and accurate segmentations of left atrium (LA) and atrial scars
from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are
in high demand for quantifying atrial scars. The previous quantification of
atrial scars relies on a two-phase segmentation for LA and atrial scars due to
their large volume difference (unbalanced atrial targets). In this paper, we
propose an inter-cascade generative adversarial network, namely JAS-GAN, to
segment the unbalanced atrial targets from LGE CMR images automatically and
accurately in an end-to-end way. Firstly, JAS-GAN investigates an adaptive
attention cascade to automatically correlate the segmentation tasks of the
unbalanced atrial targets. The adaptive attention cascade mainly models the
inclusion relationship of the two unbalanced atrial targets, where the
estimated LA acts as the attention map to adaptively focus on the small atrial
scars roughly. Then, an adversarial regularization is applied to the
segmentation tasks of the unbalanced atrial targets for making a consistent
optimization. It mainly forces the estimated joint distribution of LA and
atrial scars to match the real ones. We evaluated the performance of our
JAS-GAN on a 3D LGE CMR dataset with 192 scans. Compared with the
state-of-the-art methods, our proposed approach yielded better segmentation
performance (Average Dice Similarity Coefficient (DSC) values of 0.946 and
0.821 for LA and atrial scars, respectively), which indicated the effectiveness
of our proposed approach for segmenting unbalanced atrial targets.
