Assessment of abnormality regions associated with a disease from chest CT images
Systems and methods for assessing a disease are provided. Medical imaging data of lungs of a patient is received. The lungs are segmented from the medical imaging data and abnormality regions associated with a disease are segmented from the medical imaging data. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality regions. The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia.
1. A method comprising:
receiving medical imaging data of lungs of a patient;
segmenting the lungs from the medical imaging data;
segmenting abnormality regions associated with a disease from the medical imaging data;
calculating a volume metric of the lungs based on the segmented lungs;
calculating a volume metric of the abnormality regions based on the segmented abnormality regions; and
determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions, wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions comprises:
calculating a percent of opacity metric by dividing the volume metric of the lungs by the volume metric of the abnormality regions.
2. The method of claim 1 , wherein the disease is COVID-19 (coronavirus disease 2019) and the abnormality regions associated with COVID-19 comprise opacities of one or more of ground glass opacities (GGO), consolidation, and crazy-paving pattern.
3. The method of claim 1 , wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions further comprises:
evaluating a progression of the disease based on the volume metric of the abnormality regions, the volume metric of the lungs, a volume metric of the abnormality regions determined from prior medical imaging data acquired at a point in time prior to acquisition of the medical imaging data, and a volume metric of the lungs determined from the prior medical imaging data.
4. The method of claim 1 , wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions further comprises:
calculating a metric quantifying the disease based on the segmented lungs and the segmented abnormality regions; and
comparing the calculated metric with a metric quantifying the disease calculated based on prior medical imaging data acquired at a previous point in time than the medical imaging data.
5. The method of claim 1 , wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions further comprises:
classifying the disease as being one of COVID-19 (coronavirus disease 2019), SARS (severe acute respiratory syndrome), or MERS (middle east respiratory syndrome).
6. The method of claim 1 , wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions further comprises:
detecting presence of COVID-19 in the lungs based on the segmented lungs, the segmented abnormality regions, and patient data.
7. The method of claim 1 , wherein the disease is a viral pneumonia.
8. A method comprising:
receiving medical imaging data of lungs of a patient;
segmenting the lungs from the medical imaging data;
segmenting abnormality regions associated with a disease from the medical imaging data;
calculating a volume metric of the lungs based on the segmented lungs;
calculating a volume metric of the abnormality regions based on the segmented abnormality regions; and
determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions, wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions comprises:
calculating a percent of opacity metric for each lobe of the lungs based on a volume metric of each lobe determined from the segmented lungs and a volume metric of abnormality regions in each lobe determined from the segmented abnormality regions;
assigning each lobe with a score based on its percent of opacity metric; and
summing the scores to calculate a lung severity score.
9. The method of claim 8 , wherein the disease is COVID-19 (coronavirus disease 2019) and the abnormality regions associated with COVID-19 comprise opacities of one or more of ground glass opacities (GGO), consolidation, and crazy-paving pattern.
10. An apparatus comprising:
means for receiving medical imaging data of lungs of a patient;
means for segmenting the lungs from the medical imaging data;
means for segmenting abnormality regions associated with a disease from the medical imaging data;
means for calculating a volume metric of the lungs based on the segmented lungs;
means for calculating a volume metric of the abnormality regions based on the segmented abnormality regions; and
means for determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions, wherein the means for determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions comprises:
means for calculating a percent of opacity metric by dividing the volume metric of the lungs by the volume metric of the abnormality regions.
11. The apparatus of claim 10 , wherein the disease is COVID-19 (coronavirus disease 2019) and the abnormality regions associated with COVID-19 comprise opacities of one or more of ground glass opacities (GGO), consolidation, and crazy-paving pattern.
12. The apparatus of claim 10 , wherein the means for determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions further comprises:
means for evaluating a progression of the disease based on the volume metric of the abnormality regions, the volume metric of the lungs, a volume metric of the abnormality regions determined from prior medical imaging data acquired at a point in time prior to acquisition of the medical imaging data, and a volume metric of the lungs determined from the prior medical imaging data.
13. An apparatus comprising:
means for receiving medical imaging data of lungs of a patient;
means for segmenting the lungs from the medical imaging data;
means for segmenting abnormality regions associated with a disease from the medical imaging data;
means for calculating a volume metric of the lungs based on the segmented lungs;
means for calculating a volume metric of the abnormality regions based on the segmented abnormality regions; and
means for determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions, wherein the means for determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions comprises:
means for calculating a percent of opacity metric for each lobe of the lungs based on a volume metric of each lobe determined from the segmented lungs and a volume metric of abnormality regions in each lobe determined from the segmented abnormality regions;
means for assigning each lobe with a score based on its percent of opacity metric; and
means for summing the scores to calculate a lung severity score.
14. A non-transitory computer readable medium storing computer program instructions, the computer program instructions when executed by a processor cause the processor to perform operations comprising:
receiving medical imaging data of lungs of a patient;
segmenting the lungs from the medical imaging data;
segmenting abnormality regions associated with a disease from the medical imaging data;
calculating a volume metric of the lungs based on the segmented lungs;
calculating a volume metric of the abnormality regions based on the segmented abnormality regions; and
determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions, wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions comprises:
calculating a percent of opacity metric by dividing the volume metric of the lungs by the volume metric of the abnormality regions.
15. The non-transitory computer readable medium of claim 14 , wherein the disease is COVID-19 (coronavirus disease 2019) and the abnormality regions associated with COVID-19 comprise opacities of one or more of ground glass opacities (GGO), consolidation, and crazy-paving pattern.
16. The non-transitory computer readable medium of claim 14 , wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions comprises:
calculating a metric quantifying the disease based on the segmented lungs and the segmented abnormality regions; and
comparing the calculated metric with a metric quantifying the disease calculated based on prior medical imaging data acquired at a previous point in time than the medical imaging data.
17. The non-transitory computer readable medium of claim 14 , wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions comprises:
classifying the disease as being one of COVID-19 (coronavirus disease 2019), SARS (severe acute respiratory syndrome), or MERS (middle east respiratory syndrome).
18. The non-transitory computer readable medium of claim 14 , wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions comprises:
detecting presence of COVID-19 in the lungs based on the segmented lungs, the segmented abnormality regions, and patient data.
19. The non-transitory computer readable medium of claim 14 , wherein the disease is a viral pneumonia.
20. A non-transitory computer readable medium storing computer program instructions, the computer program instructions when executed by a processor cause the processor to perform operations comprising:
receiving medical imaging data of lungs of a patient;
segmenting the lungs from the medical imaging data;
segmenting abnormality regions associated with a disease from the medical imaging data;
calculating a volume metric of the lungs based on the segmented lungs;
calculating a volume metric of the abnormality regions based on the segmented abnormality regions; and
determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions, wherein determining an assessment of the disease based on the volume metric of the lungs and the volume metric of the abnormality regions comprises:
calculating a percent of opacity metric for each lobe of the lungs based on a volume metric of each lobe determined from the segmented lungs and a volume metric of abnormality regions in each lobe determined from the segmented abnormality regions;
assigning each lobe with a score based on its percent of opacity metric; and
summing the scores to calculate a lung severity score.