System for color-coding medical instrumentation and methods of use
AssigneeClear Biopsy LLC
InventorSteven Poplaw
Life of this patent
Estimating the term…
Abstract
A system comprising a biopsy needle device comprising a cannula comprising a distal end configured to sever a tissue sample, and a trocar disposed within the cannula comprising a notch configured to retain a tissue sample, wherein at least one of the cannula and the trocar is divided into at least two segments including different echogenic coatings, an ultrasound probe, and a processor configured and arranged to collect images from the ultrasound probe and color code the at least two segments based on at least one characteristic relating to different echogenic coatings, surface textures, surface contours and dimensions of the biopsy device.
Claims16
A computer-implemented method for using machine-learning-based image analysis to colorize at least one medical device in medical imaging, the method comprising:
- obtaining medical imaging including a depiction of at least one medical device within an anatomy of a patient, the at least one medical device including one or more of a needle, a vacuum-assisted device, a clip or marker, an RFID clip or marker, a vascular access device, a stent, a filter, an angioplasty, a drain, or a catheter;
- generating a segmentation of the at least one medical device by inputting the medical imaging and an intended procedure type into a trained machine-learning model that has been trained, based on (i) a plurality of training images of medical devices, (ii) labels of one or more portions of the medical devices, and (iii) a plurality of procedure types, such that the trained machine-learning model is configured to identify portions of the medical device corresponding to the labels;
- modifying the medical imaging by applying a color-coding to the depiction of the at least one medical device based on the identified portions, the color-coding including a different color for each portion of the at least one medical device;
- and
- causing a display to output the modified medical imaging.
A computer-implemented method for using machine-learning-based image analysis to colorize at least one medical device in medical imaging, the method comprising:
- obtaining medical imaging including a depiction of at least one medical device within an anatomy of a patient;
- generating a segmentation of the at least one medical device by inputting the medical imaging and an intended procedure type into a trained machine-learning model that has been trained, based on (i) a plurality of training images of medical devices (ii) labels of one or more portions of the medical devices, and (iii) a plurality of procedure types, such that the trained machine-learning model is configured to identify portions of the medical device corresponding to the labels, wherein the labels correspond to one or more of surface contours of the one or more portions, foci associated with the one or more portions, or different echogenicities of the one or more portions;
- modifying the medical imaging by applying a color-coding to the depiction of the at least one medical device based on the identified portions, the color-coding including a different color for each portion of the at least one medical device;
- and
- causing a display to output the modified medical imaging, wherein:
- each of different portions of the at least one medical device has a respective echogenicity that is different than each other based on a different coating applied to each of the different portions;
- and
- the labels are based on the respective echogenicities, such that the trained machine-learning model is further configured to segment the depiction of the at least one medical device in the medical imaging into portions based on relative echogenicity of the different portions.
A computer-implemented method for using machine-learning-based image analysis to colorize at least one medical device in medical imaging, the method comprising:
- obtaining medical imaging including a depiction of at least one medical device within an anatomy of a patient;
- generating a color-coding of the at least one medical device by inputting the medical imaging and an intended procedure type into a trained machine-learning model that has been trained, based on (i) a plurality of training images of medical devices (ii) labels of one or more portions of the medical devices, and (iii) a plurality of procedure types, such that the trained machine-learning model is configured identify portions of the medical device corresponding to the labels;
- modifying the medical imaging by applying a color-coding to the depiction of the at least one medical device based on the identified portions, the color-coding including a different color for each portion of the at least one medical device;
- and
- causing a display to output the modified medical imaging, wherein:
- each of different portions of the at least one medical device has a respective echogenicity that is different than each other based on a different coating applied to each of the different portions, each of the different coatings including a different density of microbubbles;
- and
- the labels are based on the respective echogenicities, such that the trained machine-learning model is further configured to segment the depiction of the at least one medical device in the medical imaging into portions based on relative echogenicity of the different portions.
Dependencies are inferred from the claim text.