20230081866. METHODS AND SYSTEMS FOR GENERATING SIMULATED INTRAOPERATIVE IMAGING DATA OF A SUBJECT simplified abstract (Stryker Corporation)

METHODS AND SYSTEMS FOR GENERATING SIMULATED INTRAOPERATIVE IMAGING DATA OF A SUBJECT

Organization Name

Stryker Corporation

Inventor(s)

Lina Gurevich of Vancouver (CA)

Benjamin Harder of Nashville TN (US)

METHODS AND SYSTEMS FOR GENERATING SIMULATED INTRAOPERATIVE IMAGING DATA OF A SUBJECT - A simplified explanation of the abstract

This abstract first appeared for US patent application 20230081866 titled 'METHODS AND SYSTEMS FOR GENERATING SIMULATED INTRAOPERATIVE IMAGING DATA OF A SUBJECT

Simplified Explanation

The present disclosure is about using machine-learning techniques to generate intraoperative fluorescence images of a subject for medical imaging purposes. This can be helpful in aiding surgeries, diagnosing diseases, and guiding treatment.

• The system receives an intraoperative white light image of the subject.
• The intraoperative white light image is input into a generator of a trained generative adversarial network (GAN) model.
• The GAN model is trained using a collection of training image pairs, where each pair consists of an intraoperative white light image and an intraoperative fluorescence image of the same tissue.
• The system obtains the generated intraoperative fluorescence image from the generator.
• The generated intraoperative fluorescence image is displayed on a display for visualization.

Potential Applications

• Assisting surgeons during surgeries by providing real-time fluorescence images of the subject's tissues.
• Aiding in the diagnosis and treatment of diseases by generating fluorescence images that can reveal specific markers or abnormalities.
• Enhancing medical imaging techniques by providing additional information through fluorescence imaging.

Problems Solved

• Lack of real-time fluorescence imaging during surgeries.
• Difficulty in visualizing specific markers or abnormalities in tissues.
• Limited information provided by traditional medical imaging techniques.

Benefits

• Real-time generation of intraoperative fluorescence images for immediate visualization.
• Improved accuracy in identifying markers or abnormalities in tissues.
• Enhanced medical imaging capabilities through the combination of white light and fluorescence imaging.

Original Abstract Submitted

the present disclosure relates generally to medical imaging, and more specifically to machine-learning techniques to generate intraoperative fluorescence images of a subject (e.g., to aid a surgery, to aid diagnosis and treatment of diseases). the system can receive an intraoperative white light image of the subject, input the intraoperative white light image of the subject into a generator of a trained generative adversarial network (gan) model trained. in some examples, the gan model is trained using a plurality of training image pairs, and each training image pair comprises an intraoperative white light training image and an intraoperative fluorescence training image of a same tissue. the system can obtain, from the generator, the generated intraoperative fluorescence image of the subject and display, on a display, the generated intraoperative fluorescence image of the subject.