Hua Li

US Patent:

20190314644, Oct 17, 2019

Filed:

Jun 25, 2019

Appl. No.:

16/451760

Inventors:

- St. Louis MO, US
Sasa Mutic - Creve Coeur MO, US
Jun Tan - Coppell TX, US
Michael Altman - University City MO, US
James Kavanaugh - University City MO, US
Hua Li - Chesterfield MO, US

International Classification:

A61N 5/10
G06T 7/13

Abstract:

A system and method for validating the accuracy of delineated contours in computerized imaging using statistical data for generating assessment criterion that define acceptable tolerances for delineated contours, with the statistical data being conditionally updated and/or refined between individual processes for validating delineated contours to thereby adjust the tolerances defined by the assessment criterion in the stored statistical data, such that the stored statistical data is more closely representative of a target population. In an alternative embodiment, a system and method for validating the accuracy of delineated contours in computerized imaging using machine learning for assessing delineated contours, with the machine learning training data being used to generate geometric attributes, and the geometric attributes used to construct intra- and interstructural geometric attribute distribution models to automatically detect contouring errors. The present invention may be used to facilitate, as one example, radiation therapy.

US Patent:

20150297916, Oct 22, 2015

Filed:

Apr 30, 2015

Appl. No.:

14/700592

Inventors:

- St. Louis MO, US
Sasa Mutic - Creve Coeur MO, US
Jun Tan - Coppell TX, US
Michael Altman - University City MO, US
James Kavanaugh - University City MO, US
Hua Li - Chesterfield MO, US

International Classification:

A61N 5/10

Abstract:

A system and method for validating the accuracy of delineated contours in computerized imaging using statistical data for generating assessment criterion that define acceptable tolerances for delineated contours, with the statistical data being conditionally updated and/or refined between individual processes for validating delineated contours to thereby adjust the tolerances defined by the assessment criterion in the stored statistical data, such that the stored statistical data is more closely representative of a target population. In an alternative embodiment, a system and method for validating the accuracy of delineated contours in computerized imaging using machine learning for assessing delineated contours, with the machine learning training data being used to generate geometric attributes, and the geometric attributes used to construct intra- and interstructural geometric attribute distribution models to automatically detect contouring errors. The present invention may be used to facilitate, as one example, radiation therapy.