  Accurate lung lesion segmentation from Computed Tomography (CT) images is
crucial to the analysis and diagnosis of lung diseases such as COVID-19 and
lung cancer. However, the smallness and variety of lung nodules and the lack of
high-quality labeling make the accurate lung nodule segmentation difficult. To
address these issues, we first introduce a novel segmentation mask named Soft
Mask which has richer and more accurate edge details description and better
visualization and develop a universal automatic Soft Mask annotation pipeline
to deal with different datasets correspondingly. Then, a novel Network with
detailed representation transfer and Soft Mask supervision (DSNet) is proposed
to process the input low-resolution images of lung nodules into high-quality
segmentation results. Our DSNet contains a special Detail Representation
Transfer Module (DRTM) for reconstructing the detailed representation to
alleviate the small size of lung nodules images, and an adversarial training
framework with Soft Mask for further improving the accuracy of segmentation.
Extensive experiments validate that our DSNet outperforms other
state-of-the-art methods for accurate lung nodule segmentation and has strong
generalization ability in other accurate medical segmentation tasks with
competitive results. Besides, we provide a new challenging lung nodules
segmentation dataset for further studies.
