A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images

• Published in: IEEE transactions on medical imaging Vol. 39; no. 8; pp. 2653 - 2663
• Main Authors: Wang, Guotai, Liu, Xinglong, Li, Chaoping, Xu, Zhiyong, Ruan, Jiugen, Zhu, Haifeng, Meng, Tao, Li, Kang, Huang, Ning, Zhang, Shaoting
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Annotations; Betacoronavirus; Computed tomography; convolutional neural network; Coronavirus Infections - diagnostic imaging; Coronaviruses; COVID-19; Deep Learning; Humans; Image processing; Image segmentation; Labels; Learning; Lesions; Lung; Lung - diagnostic imaging; Machine learning; Medical imaging; Noise; Noise measurement; Noise standards; noisy label; Pandemics; Pneumonia; Pneumonia, Viral - diagnostic imaging; Robustness; SARS-CoV-2; segmentation; Teachers; Tomography, X-Ray Computed - methods; Training
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2020.3000314

Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation.