Accurate lung nodule segmentation from Computed Tomography
(CT) images is crucial to the analysis and diagnosis
of lung diseases such as COVID-19 and lung cancer. However,
due to the variety of lung nodules and the lack of highquality
labeling, accurate lung nodule segmentation is still
a challenging problem. In this paper, we propose a novel
paradigm including an automatic accurate annotation pipeline
and a segmentation network for this task. First, we introduce
a new segmentation mask representation named Soft Mask
which has richer and more accurate edge details description
and better visualization, and we design a universal automatic
Soft Mask annotation pipeline to deal with different datasets.
Besides, we provide a new challenging lung nodules segmentation
dataset with traditional binarized masks and our soft
masks for further studies. Second, we propose an effective
network called SoftGAN that includes an improved backbone
and an adversarial training framework with Soft Mask,
in order to improve the performance of accurate lung nodules
segmentation. Extensive experiments validate that our Soft-
GAN outperforms the state-of-the-art methods for accurate
lung nodule segmentation.