ABSTRACT
Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.
ABSTRACT
BACKGROUND & AIMS: Coronavirus-19 disease (COVID-19) is associated with hepatocellular liver injury of uncertain significance. We aimed to determine whether development of significant liver injury during hospitalization is related to concomitant medications or processes common in COVID-19 (eg, ischemia, hyperinflammatory, or hypercoagulable states), and whether it can result in liver failure and death. METHODS: There were 834 consecutive patients hospitalized with COVID-19 who were included. Clinical, medication, and laboratory data were obtained at admission and throughout hospitalization using an identified database. Significant liver injury was defined as an aspartate aminotransferase (AST) level 5 or more times the upper limit of normal; ischemia was defined as vasopressor use for a minimum of 2 consecutive days; hyperinflammatory state was defined as high-sensitivity C-reactive protein value of 100 mg/L or more, and hypercoagulability was defined as D-dimer 5 mg/L or more at any time during hospitalization. RESULTS: A total of 105 (12.6%) patients developed significant liver injury. Compared with patients without significant liver injury, ischemia (odds ratio [OR], 4.3; range, 2.5-7.4; P < .0001) and tocilizumab use (OR, 3.6; range, 1.9-7.0; P = .0001) were independent predictors of significant liver injury. Although AST correlated closely with alanine aminotransferase (R = 0.89) throughout hospitalization, AST did not correlate with the international normalized ratio (R = 0.10) or with bilirubin level (R = 0.09). Death during hospitalization occurred in 136 (16.3%) patients. Multivariate logistic regression showed that significant liver injury was not associated with death (OR, 1.4; range, 0.8-2.6; P = .2), while ischemic (OR, 2.4; range, 1.4-4.0; P = .001), hypercoagulable (OR, 1.7; range, 1.1-2.6; P = .02), and hyperinflammatory (OR, 1.9; range, 1.2-3.1; P = .02) disease states were significant predictors of death. CONCLUSIONS: Liver test abnormalities known to be associated with COVID-19 are secondary to other insults, mostly ischemia or drug-induced liver injury, and do not lead to liver insufficiency or death.