Background and Objective: 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 and a better strategy to train a robust deep learning model for COVID-19 infection segmentation Methods: 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 i) We introduce the multi-task learning method to get a multi-lesion pre-trained model for COVID-19 infection ii) We propose and compare four transfer learning strategies with various performance gains and training time costs Our proposed Hybrid-encoder Learning strategy introduces a Dedicated-encoder and an Adapted-encoder to extract COVID-19 infection features and general lung lesion features, respectively An attention-based Selective Fusion unit is designed for dynamic feature selection and aggregation Results: Experiments show that trained with limited data, proposed Hybrid-encoder strategy based on multi-lesion pre-trained model achieves a mean DSC, NSD, Sensitivity, F1-score, Accuracy and MCC of 0 704, 0 735, 0 682, 0 707, 0 994 and 0 716, respectively, with better genetalization and lower over-fitting risks for segmenting COVID-19 infection Conclusions: 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
Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation