  The manual ground truth of abdominal multi-organ is labor-intensive. In order
to make full use of CT data, we developed a semi-supervised learning based
dual-light UNet. In the training phase, it consists of two light UNets, which
make full use of label and unlabeled data simultaneously by using
consistent-based learning. Moreover, separable convolution and residual
concatenation was introduced light UNet to reduce the computational cost.
Further, a robust segmentation loss was applied to improve the performance. In
the inference phase, only a light UNet is used, which required low time cost
and less GPU memory utilization. The average DSC of this method in the
validation set is 0.8718. The code is available in
https://github.com/laihaoran/Semi-SupervisednnUNet.
