  Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading
all around the world. Deep learning has been adopted as an effective technique
to aid COVID-19 detection and segmentation from computed tomography (CT)
images. The major challenge lies in the inadequate public COVID-19 datasets.
Recently, transfer learning has become a widely used technique that leverages
the knowledge gained while solving one problem and applying it to a different
but related problem. However, it remains unclear whether various non-COVID19
lung lesions could contribute to segmenting COVID-19 infection areas and how to
better conduct this transfer procedure. This paper provides a way to understand
the transferability of non-COVID19 lung lesions. Based on a publicly available
COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four
transfer learning methods using 3D U-Net as a standard encoder-decoder method.
The results reveal the benefits of transferring knowledge from non-COVID19 lung
lesions, and learning from multiple lung lesion datasets can extract more
general features, leading to accurate and robust pre-trained models. We further
show the capability of the encoder to learn feature representations of lung
lesions, which improves segmentation accuracy and facilitates training
convergence. In addition, our proposed Hybrid-encoder learning method
incorporates transferred lung lesion features from non-COVID19 datasets
effectively and achieves significant improvement. These findings promote new
insights into transfer learning for COVID-19 CT image segmentation, which can
also be further generalized to other medical tasks.
