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

IEEE Trans Med Imaging. 2020 Aug;39(8):2653-2663. doi: 10.1109/TMI.2020.3000314.

Abstract

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.

MeSH terms

  • Algorithms
  • Betacoronavirus
  • COVID-19
  • Coronavirus Infections / diagnostic imaging*
  • Deep Learning*
  • Humans
  • Lung / diagnostic imaging
  • Pandemics
  • Pneumonia, Viral / diagnostic imaging*
  • SARS-CoV-2
  • Tomography, X-Ray Computed / methods*

Grants and funding

This work was supported by the National Natural Science Foundation of China under Grant 81771921 and Grant 61901084.