COVID-view: Diagnosis of COVID-19 using Chest CT

Shreeraj Jadhav, Gaofeng Deng, Marlene Zawin, Arie Kaufman

View presentation:2021-10-29T15:00:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
COVID-view is an elaborate visualization application for diagnosis of COVID-19 patients using chest CT. (a) 3D visualization of lungs and a ground glass opacity (GGO) region. (b) Activation heatmap from our classification model trained to classify CT scans into COVID positive and negative. (c) masked MIP view showing GGO and highlighted abnormal regions.
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Significant work has been done towards deep learning (DL) models for automatic lung and lesion segmentation and classification of COVID-19 on chest CT data. However, comprehensive visualization systems focused on supporting the dual visual+DL diagnosis of COVID-19 are non-existent. We present COVID-view, a visualization application specially tailored for radiologists to diagnose COVID-19 from chest CT data. The system incorporates a complete pipeline of automatic lungs segmentation, localization/isolation of lung abnormalities, followed by visualization, visual and DL analysis, and measurement/quantification tools. Our system combines the traditional 2D workflow of radiologists with newer 2D and 3D visualization techniques with DL support for a more comprehensive diagnosis. COVID-view incorporates a novel DL model for classifying the patients into positive/negative COVID-19 cases, which acts as a reading aid for the radiologist using COVID-view, and provides the attention heatmap as an explainable DL for the model output. We designed and evaluated COVID-view through suggestions, close feedback and conducting case studies of real-world patient data by expert radiologists who have substantial experience diagnosing chest CT scans for COVID-19, pulmonary embolism, and other forms of lung infections. We present requirements and task analysis for the diagnosis of COVID-19 that motivate our design choices and results in a practical system which is capable of handling real-world patient cases.