Context driven image mining to generate image-based biomarkers
First Claim
1. A method comprising:
- acquiring pixel values of medical images of patients;
generating objects of a data network for each of the medical images by linking selected pixel values to selected objects according to a class network and a process hierarchy;
measuring the objects to obtain a value for each of a plurality of image features;
selecting a first subset of the image features that belong to a first image-based biomarker;
correlating the first image-based biomarker for each of the patients with a clinical endpoint observed for that patient;
displaying on a graphical user interface how the first image-based biomarker correlates with the clinical endpoint observed for each of the patients;
identifying a subgroup of the patients for which the first image-based biomarker correlates poorly with the clinical endpoints observed for the patients in the subgroup; and
selecting a second subset of the image features that belong to a second image-based biomarker that better correlates with the clinical endpoints observed for the patients in the subgroup.
1 Assignment
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Abstract
An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.
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Citations
15 Claims
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1. A method comprising:
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acquiring pixel values of medical images of patients; generating objects of a data network for each of the medical images by linking selected pixel values to selected objects according to a class network and a process hierarchy; measuring the objects to obtain a value for each of a plurality of image features; selecting a first subset of the image features that belong to a first image-based biomarker; correlating the first image-based biomarker for each of the patients with a clinical endpoint observed for that patient; displaying on a graphical user interface how the first image-based biomarker correlates with the clinical endpoint observed for each of the patients; identifying a subgroup of the patients for which the first image-based biomarker correlates poorly with the clinical endpoints observed for the patients in the subgroup; and selecting a second subset of the image features that belong to a second image-based biomarker that better correlates with the clinical endpoints observed for the patients in the subgroup. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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Specification