This technology provides an objective, quantitative, robust method to evaluate lung morphometry and extract unbiased categorization of the lung parenchyma.
Lung morphometry analysis is used to evaluate lung physiology, pathology and development. Techniques and software currently designed to perform morphometry analysis, however, suffer from overly simplistic implementations and biased approaches. The existing parameters, such as mean linear intercept and destructive index, are subjective and limited in their ability to interpret data. Therefore, there is a need for an objective, quantitative, robust method to evaluate lung morphometry and extract and unbiased categorization of the lung parenchyma for better understanding of lung physiology and pathology.
This technology allows for improved understanding of lung physiology and pathology via an unbiased, reproducible and quantitative approach. To do this, normally time-consuming and subjective tasks are converted to standardized and computerized processes. The combination of machine learning and computer vision provided by this technology make it possible to perform complex analyses, such as accurately defining the actual size and count of alveolus in samples. Therefore, this technology provides an improved method for accurate parenchymal airspace profiling and quantification of parenchymal destruction.
This technology has been validated with images of fixed, inflated, serial sections of a mouse lung and mouse lung with emphysema.
Jeanine D’Armiento, M.D., Ph.D.
IR CU16048
Licensing Contact: Sara Gusik