COVID-19 Infection Segmentation and Severity Assessment Using a Self-Supervised Learning Approach

Background: Automated segmentation of COVID-19 infection lesions and the assessment of the severity of the infections are critical in COVID-19 diagnosis and treatment. Based on a large amount of annotated data, deep learning approaches have been widely used in COVID-19 medical image analysis. However, the number of medical image samples is generally huge, and it is challenging to obtain enough annotated medical images for training a deep CNN model. Methods: To address these challenges, we propose a novel self-supervised deep learning method for automated segmentation of COVID-19 infection lesions and assessing the severity of infection, which can reduce the dependence on the annotation of the training samples. In the proposed method, first, many unlabeled data are used to pre-train an encoder-decoder model to learn rotation-dependent and rotation-invariant features. Then, a small amount of labeled data is used to fine-tune the pre-trained encoder-decoder for COVID-19 severity classification and lesion segmentation. Results: The proposed methods were tested on two public COVID-19 CT datasets and one self-built dataset. Accuracy, precision, recall, and F1-score were used to measure classification performance and Dice coefficient was used to measure segmentation performance. For COVID-19 severity classification, the proposed method outperformed other unsupervised feature learning methods by about 7.16% in accuracy. For segmentation, when the amount of labeled data was 100%, the Dice value of the proposed method was 5.58% higher than that of U-Net.; in 70% of the cases, our method was 8.02% higher than U-Net; in 30% of the cases, our method was 11.88% higher than U-Net; and in 10% of the cases, our method was 16.88% higher than U-Net. Conclusions: The proposed method provides better classification and segmentation performance under limited labeled data than other methods.