COVID-view: Diagnosis of COVID-19 using Chest CT
- URL: http://arxiv.org/abs/2108.03799v1
- Date: Mon, 9 Aug 2021 04:19:25 GMT
- Title: COVID-view: Diagnosis of COVID-19 using Chest CT
- Authors: Shreeraj Jadhav, Gaofeng Deng, Marlene Zawin, Arie E. Kaufman
- Abstract summary: COVID-view is 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.
- Score: 14.039366815365288
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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