Automated 3D modeling of diseased livers using artificial intelligence

This technology is an automated method to create accurate 3D models of both normal and diseased livers with heterogeneous tissue, using deep learning and artificial intelligence.

Unmet Need: Accurate 3D model of diseased livers with heterogenous tissue

The ability to model and separate liver tissue from the surrounding tissue in a CT scan is important for medical interventions such as tumor resection, transplantation, and arthroscopic surgery. The current gold standard uses active contour models or deep learning convolution networks to automatically segment liver tissues. However, active contour models only work well for homogenous liver tissue and perform poorly in heterogenous liver tissue, often seen in livers with tumors, cirrhosis, or partial resection. Convolution networks outperform the active contour models, but they suffer from overfitting of data and thus may not be general enough to model all liver types.

The Technology: Automated segmentation of heterogeneous liver tissue in CT imaging data

This technology is a software that provides a 3D model of a liver by segmenting images of liver tissue obtained from CT imaging scans. The method used is a combination of both deep learning algorithms and pre-existing deformable models. The technology utilizes convolution neural networks to recognize the liver tissue and active contour models to create a smooth segmentation border through analysis of low-level contour imaging data.

Applications:

  • Automated segmentation of liver tissue in CT scan data
  • Expansion to other tissue types in CT, MRI, and ultrasound
  • Automated 3D modeling of diseased livers
  • Automated recognition and modeling of other organs
  • Accurate modeling of diseased livers with heterogenous tissue
  • Non-invasive tracking of changes in liver state over time

Advantages:

  • Specifically models diseased livers
  • Non-invasive screenings
  • Automated and unbiased analysis algorithm
  • Utilizes existing imaging and modeling technology

Lead Inventors:

Binsheng Zhao, D.Sc.

Xiaotao Guo, Ph.D.

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