  Most of the state-of-the-art semantic segmentation reported in recent years
is based on fully supervised deep learning in the medical domain. How?ever, the
high-quality annotated datasets require intense labor and domain knowledge,
consuming enormous time and cost. Previous works that adopt semi?supervised and
unsupervised learning are proposed to address the lack of anno?tated data
through assisted training with unlabeled data and achieve good perfor?mance.
Still, these methods can not directly get the image annotation as doctors do.
In this paper, inspired by self-training of semi-supervised learning, we
pro?pose a novel approach to solve the lack of annotated data from another
angle, called medical image pixel rearrangement (short in MIPR). The MIPR
combines image-editing and pseudo-label technology to obtain labeled data. As
the number of iterations increases, the edited image is similar to the original
image, and the labeled result is similar to the doctor annotation. Therefore,
the MIPR is to get labeled pairs of data directly from amounts of unlabled data
with pixel rearrange?ment, which is implemented with a designed conditional
Generative Adversarial Networks and a segmentation network. Experiments on the
ISIC18 show that the effect of the data annotated by our method for
segmentation task is is equal to or even better than that of doctors
annotations
