Deep-learning-based hepatic fat assessment
AssigneesCase Western Reserve University, Emory University, University Hospitals Cleveland Medical Center
InventorsGourav Modanwal, Sadeer Al-Kindi, Jonathan Walker, Rohan Dhamdhere, Sanjay Rajagopalan, Anant Madabhushi
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Abstract
The present disclosure relates to a method. The method includes accessing automatically segmented liver data and automatically segmented spleen data from a patient. The automatically segmented liver data is used to determine a liver attenuation and the automatically segmented spleen data is used to determine a spleen attenuation. A liver-to-spleen attenuation ratio is determined from the liver attenuation and the spleen attenuation. A hepatic steatosis determination is made from the liver-to-spleen attenuation ratio.
Claims20
A method, comprising:
- accessing automatically segmented liver data and automatically segmented spleen data from a patient, wherein the automatically segmented liver data comprises a region of interest from a digitized image of a liver and the automatically segmented spleen data comprises a region of interest from a digitized image of a spleen;
- utilizing the automatically segmented liver data to determine a liver attenuation;
- utilizing the automatically segmented spleen data to determine a spleen attenuation;
- determining a liver-to-spleen attenuation ratio from the liver attenuation and the spleen attenuation;
- and
- making a hepatic steatosis determination from the liver-to-spleen attenuation ratio.
A non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising:
- operating one or more deep learning models on one or more computed tomography (CT) images comprising a liver and a spleen, wherein the one or more deep learning models are configured to segment the liver and generate automatically segmented liver data and to segment the spleen and generate automatically segmented spleen data;
- measuring a liver attenuation from the automatically segmented liver data;
- measuring a spleen attenuation from the automatically segmented spleen data;
- determining a liver-to-spleen attenuation ratio from the liver attenuation and the spleen attenuation;
- and
- generating a hepatic steatosis determination from the liver-to-spleen attenuation ratio.
An apparatus, comprising:
- one or more deep learning models configured to operate upon one or more digitized images that include a liver and a spleen to generate automatically segmented liver data and automatically segmented spleen data;
- an attenuation calculation tool configured to utilize the automatically segmented liver data to measure a liver attenuation, to utilize the automatically segmented spleen data to measure a spleen attenuation, and to determine a liver-to-spleen attenuation ratio from the liver attenuation and the spleen attenuation;
- and
- a hepatic steatosis calculation tool configured to generate a hepatic steatosis (HS) determination by comparing the liver-to-spleen attenuation ratio to an hepatic steatosis threshold.
Dependencies are inferred from the claim text.