US 12,731,252 B2Granted

Systems and methods for image guided tissue ablation

AssigneeGynesonics, Inc.

InventorJiayu Chen

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Abstract

A real-time, intra-uterine operating field image is displayed to an operator. A processor identifies anatomical features and marks anatomical boundaries on the image with the aid of a trained classifier model. Labels for the identified anatomical features and the anatomical boundaries are displayed on the image. The trained classifier model is trained based on a set of prior surgical field images that have anatomical features and anatomical boundaries therein identified and marked, respectively, by previous operators. The trained classifier model is typically a convolutional deep neural network. The operator is allowed to modify the anatomical feature labels and anatomical boundaries, as well as update the set of training images with the modified image. The labeling of the anatomical features and establishment of the anatomical features facilitate performing treatments in the uterus, such as the ablation of uterine fibroids.

Claims22

Dependent claims

A computer-implemented method of planning and implementing a treatment procedure, the method comprising:

(i) displaying a real-time operating field image to a user;
(ii) identifying one or more of (a) at least one anatomical feature or (b) at least one anatomical boundary on the real-time operating field image, with the aid of the trained classifier model;
(iii) displaying one or more of (a) at least one label for the identified at least one anatomical feature or (b) at least one marking for the identified at least one anatomical boundary on the displayed real-time operating field image;
(iv) allowing the user to modify the real-time operating field image by one or more of (a) re-labelling the identified at least one anatomical feature or (b) re-positioning the at least one marking for the at least one anatomical boundary;
and
(v) providing an update to the trained classifier model based on the modifications by the user;
wherein the trained classifier model is generated by:
(vi) displaying an operating field image to a user;
(vii) establishing the at least one anatomical boundary in the operating field image, the at least one anatomical boundary being marked by the user;
(viii) labelling the at least one anatomical feature separated by the at least one anatomical boundary in the operating field image, the at least one anatomical feature being labeled by user without the aid of the classifier model;
and
(ix) repeating steps (vi) to (viii) to generate a training set of operating field images each with the at least one marked anatomical boundary and the at least one labeled anatomical feature;
and
(x) training the classifier model, based on the training set of operating field images, to identify (a) the at least one anatomical feature or (b) the at least one anatomical boundary on the realtime operating image.

11 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/306,954Filed 25 Apr 2023 · continuation
Priority: 27 Oct 2020
Published: US 2023/0260121 A117 Aug 2023
Granted: 8 Sep 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification32 codes

G06T 7/0012
Inspection of images, e.g. flaw detection: biomedical image inspection
G16H 50/20
ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or…: for…
A61B 8/0841
Involving detecting or locating foreign bodies or organic structures: for locating instruments
A61B 8/483
Diagnostic techniques: involving the acquisition of a 3D volume of data