View source for 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==

[[:Category:Stryker Corporation|Stryker Corporation]]


[[Category:Stryker Corporation]]

==Inventor(s)==

[[:Category:Lina Gurevich of Vancouver (CA)|Lina Gurevich of Vancouver (CA)]][[Category:Lina Gurevich of Vancouver (CA)]]

[[:Category:Benjamin Harder of Nashville TN (US)|Benjamin Harder of Nashville TN (US)]][[Category: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.



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