  We consider unsupervised cell nuclei segmentation in this paper. Exploiting
the recently-proposed unpaired image-to-image translation between cell nuclei
images and randomly synthetic masks, existing approaches, e.g., CycleGAN, have
achieved encouraging results. However, these methods usually take a two-stage
pipeline and fail to learn end-to-end in cell nuclei images. More seriously,
they could lead to the lossy transformation problem, i.e., the content
inconsistency between the original images and the corresponding segmentation
output. To address these limitations, we propose a novel end-to-end
unsupervised framework called Aligned Disentangling Generative Adversarial
Network (AD-GAN). Distinctively, AD-GAN introduces representation
disentanglement to separate content representation (the underling spatial
structure) from style representation (the rendering of the structure). With
this framework, spatial structure can be preserved explicitly, enabling a
significant reduction of macro-level lossy transformation. We also propose a
novel training algorithm able to align the disentangled content in the latent
space to reduce micro-level lossy transformation. Evaluations on real-world 2D
and 3D datasets show that AD-GAN substantially outperforms the other comparison
methods and the professional software both quantitatively and qualitatively.
Specifically, the proposed AD-GAN leads to significant improvement over the
current best unsupervised methods by an average 17.8% relatively (w.r.t. the
metric DICE) on four cell nuclei datasets. As an unsupervised method, AD-GAN
even performs competitive with the best supervised models, taking a further
leap towards end-to-end unsupervised nuclei segmentation.
