ABSTRACT
Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.
Subject(s)
COVID-19 , Lung DiseasesABSTRACT
Background: Computed tomography (CT) characteristics associated with critical outcomes of patients with coronavirus disease 2019 (COVID-19) have been reported. However, CT risk factors for mortality are poorly understood. We aimed to investigate the automatically quantified CT imaging predictors for COVID-19 mortality.Methods: In this retrospective study, laboratory-confirmed COVID-19 patients at Wuhan Central Hospital between December 9, 2019, and March 19, 2020, were included. A novel prognostic biomarker, V-HU score, depicting the volume of total pneumonia infection and the average Hounsfield unit (HU) value of consolidation areas was quantified from CT by an artificial intelligence (AI) system. Cox proportional hazards models were used to investigate risk factors for mortality.Findings: This study included 238 patients (126 survivors and 112 non-survivors). The V-HU marker was an independent predictor (hazard ratio [HR] 2·78, 95% CI 1·50-5·17; p=0·0012) after adjusting for several COVID-19 prognostic indicators significant in univariable analysis. The prognostic performance of the model containing clinical and outpatient laboratory factors was improved by integrating the V-HU marker (c-index: 0·695 versus 0·728; p<0·0001). Older patients (age>=65 years; HR 3·56, 95% CI 1·64-7·71; p=0·0006) and younger patients (age<65 years; HR 4·60, 95% CI 1·92-10·99; p<0·0001) could be risk-stratified by the V-HU marker.Interpretation: A combination of an increased volume of total pneumonia infection and high HU value of consolidation areas showed a strong correlation to COVID-19 mortality, as determined by AI quantified CT. The novel radiologic marker may be used for early risk assessment to prioritize critical care resources for patients at a high risk of mortality.Funding: None.Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: The study was approved by the Research Ethics Commission of Wuhan Central Hospital, and the requirement for writing informed consent was waived by the Ethics Commission for the emergence of infectious diseases.