Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images.

At the end of 2019, a novel coronavirus, COVID-19, was ravaging the world, wreaking havoc on public health and the global economy. Today, although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for COVID-19 clinical diagnosis, it is a time-consuming and labor-intensive procedure. Simultaneously, an increasing number of individuals are seeking for better alternatives to RT-PCR. As a result, automated identification of COVID-19 lung infection in computed tomography (CT) images may help traditional diagnostic approaches in determining the severity of the disease. Unfortunately, a shortage of labeled training sets makes using AI deep learning algorithms to accurately segregate diseased regions in CT scan challenging. We design a simple and effective weakly supervised learning strategy for COVID-19 CT image segmentation to overcome the segmentation issue in the absence of adequate labeled data, namely LLC-Net. Unlike others weakly supervised work that uses a complex training procedure, our LLC-Net is relatively easy and repeatable. We propose a Local Self-Coherence Mechanism to accomplish label propagation based on lesion area labeling characteristics for weak labels that cannot offer comprehensive lesion areas, hence forecasting a more complete lesion area. Secondly, when the COVID-19 training samples are insufficient, the Scale Transform for Self-Correlation is designed to optimize the robustness of the model to ensure that the CT images are consistent in the prediction results from different angles. Finally, in order to constrain the segmentation accuracy of the lesion area, the Lesion Infection Edge Attention Module is used to improve the information expression ability of edge modeling. Experiments on public datasets demonstrate that our method is more effective than other weakly supervised methods and achieves a new state-of-the-art performance.