公开(公告)号：US11854195B2

公开(公告)日：2023-12-26

申请号：US17403929

申请日：2021-08-17

Applicant: NEC Corporation Of America

Inventor： Yael Schwartzbard ,  Yaacov Hoch ,  Tsvi Lev

IPC: G06T7/00 ,  A61B6/00 ,  G06T7/70 ,  G06T7/11 ,  G06T3/40 ,  G06T3/60 ,  A61B6/03 ,  G06F18/214 ,  G06F18/2415 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06T7/0012 ,  A61B6/032 ,  A61B6/5217 ,  G06F18/214 ,  G06F18/2415 ,  G06T3/40 ,  G06T3/60 ,  G06T7/11 ,  G06T7/70 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/10132 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06V2201/03

Abstract: There is provided a computed implemented method of automatically generating an adapted presentation of at least one candidate anomalous object detected from anatomical imaging data of a target individual, comprising: providing anatomical imaging data of the target individual acquired by an anatomical imaging device, analyzing the anatomical imaging data by a detection classifier for detecting at least one candidate anomalous object of the anatomical imaging data and computed associated location thereof, computing, by a presentation parameter classifier, at least one presentation parameter for adapting a presentation of a sub-set of the anatomical imaging data including the at least one candidate anomalous object according to at least the location of the candidate anomalous object, and generating according to the at least one presentation parameter, an adapted presentation of the sub-set of the anatomical imaging data including the at least one candidate anomalous object.

公开(公告)号：US20230410483A1

公开(公告)日：2023-12-21

申请号：US18304330

申请日：2023-04-20

Applicant: Bristol-Myers Squibb Company

Inventor： Zekai Chen ,  Kevin Alex Brown

IPC: G06V10/774 ,  G06T7/11 ,  G06V10/776 ,  G06T7/00 ,  G06V10/26 ,  G06T9/00 ,  G06N3/0455 ,  G06N3/0895

CPC classification number: G06V10/7753 ,  G06T7/11 ,  G06V10/776 ,  G06T7/0012 ,  G06V10/26 ,  G06T9/002 ,  G06N3/0455 ,  G06N3/0895 ,  G06V2201/03 ,  G06T2207/20021 ,  G06T2207/30096 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2200/04 ,  G16H30/20

Abstract: A method includes obtaining a first training data set including unannotated multi-dimensional medical images and executing a self-supervised masked image modeling (MIM) training process to pre-train an image encoder on the first training data set. The method also includes obtaining a second training data set that includes annotated multi-dimensional medical images. Here, each annotated multi-dimensional medical image includes a plurality of image voxels each paired with a corresponding ground-truth label indicating a class the corresponding image voxel belongs to. The method also includes executing a supervised training process to train an image analysis model on the second training data set to teach the image analysis model to learn how to predict the corresponding ground-truth labels for the plurality of image voxels of each annotated multi-dimensional medical image. The image analysis model incorporates the pre-trained image encoder.

公开(公告)号：US20230410317A1

公开(公告)日：2023-12-21

申请号：US18359821

申请日：2023-07-26

Applicant: Axial Medical Printing Limited

Inventor： Niall HASLAM ,  Lorenzo TROJAN ,  Daniel CRAWFORD

IPC: G06T7/11 ,  G06T17/20 ,  G06T7/00 ,  G16H30/40 ,  G16H50/70 ,  G16H50/50 ,  G06F18/24 ,  G06V10/26

CPC classification number: G06T7/11 ,  G06T17/20 ,  G06T7/0014 ,  G16H30/40 ,  G16H50/70 ,  G06V2201/03 ,  G06F18/24 ,  G06V10/26 ,  G06T2200/08 ,  G06T2207/30004 ,  G16H50/50

Abstract: There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.

公开(公告)号：US11847819B2

公开(公告)日：2023-12-19

申请号：US17281472

申请日：2019-12-19

Applicant: Brainlab AG

Inventor： Stefan Vilsmeier ,  Jens Schmaler

IPC: G06V10/764 ,  G06T7/136 ,  G06V20/69 ,  G06V10/774 ,  G06V10/82 ,  G06T7/00 ,  G06T7/70 ,  G06V20/70 ,  G06V10/26

CPC classification number: G06V10/764 ,  G06T7/0014 ,  G06T7/136 ,  G06T7/70 ,  G06V10/26 ,  G06V10/774 ,  G06V10/82 ,  G06V20/695 ,  G06V20/698 ,  G06V20/70 ,  G06T2207/10056 ,  G06T2207/10061 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06V2201/03

Abstract: Disclosed is a computer-implemented method which encompasses registering a tracked imaging device such as a microscope having a known viewing direction and an atlas to a patient space so that a transformation can be established between the atlas space and the reference system for defining positions in images of an anatomical structure of the patient. Labels are associated with certain constituents of the images and are input into a learning algorithm such as a machine learning algorithm, for example a convolutional neural network, together with the medical images and an anatomical vector and for example also the atlas to train the learning algorithm for automatic segmentation of patient images generated with the tracked imaging device. The trained learning algorithm then allows for efficient segmentation and/or labelling of patient images without having to register the patient images to the atlas each time, thereby saving on computational effort.

公开(公告)号：US11832962B2

公开(公告)日：2023-12-05

申请号：US17883635

申请日：2022-08-09

Applicant: INHANDPLUS INC.

Inventor： Hwiwon Lee ,  Sang Pil Yoo

IPC: G06K9/00 ,  A61B5/00 ,  G06T7/20 ,  H04W76/10 ,  G08C17/02 ,  G06T7/246 ,  G16H50/20 ,  G16H20/10 ,  G06N20/00 ,  G06V20/40 ,  G06V10/94 ,  G06V40/20 ,  H04N5/765 ,  G16H40/63 ,  G06F18/214

CPC classification number: A61B5/4833 ,  G06F18/2148 ,  G06N20/00 ,  G06T7/20 ,  G06T7/251 ,  G06V10/95 ,  G06V20/41 ,  G06V40/20 ,  G08C17/02 ,  G16H20/10 ,  G16H40/63 ,  G16H50/20 ,  H04N5/765 ,  H04W76/10 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/30196 ,  G06V2201/03

Abstract: Provided is a server for determining whether medication has been administered, the server including: a transceiver receiving a video recorded by a wearable device; a memory storing a detection model and a confirmation model, wherein the detection model is trained to output whether each of preset targets appears in an image, and the confirmation model is trained to output whether medication has been administered, wherein the preset targets include an object related to a medicine or a medicine container and a posture related to medication administration; and one or more processors configured to detect the preset targets by inputting image frames of the video to the detection model and to determine whether medication has been administered by inputting confirmation model input data to the confirmation model, the confirmation model input data generated based on a detection result of the detection model.

公开(公告)号：US11830190B2

公开(公告)日：2023-11-28

申请号：US17827399

申请日：2022-05-27

Applicant: ADIUVO DIAGNOSTICS PRIVATE LIMITED

Inventor： Bala Pesala ,  Geethanjali Radhakrishnan ,  Bikki Kumar Sha ,  John King

IPC: G06T7/00 ,  G06V10/10 ,  G06V10/82 ,  G06T7/50 ,  G06V10/60

CPC classification number: G06T7/0012 ,  G06T7/50 ,  G06V10/17 ,  G06V10/60 ,  G06V10/82 ,  G06T2207/10064 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30088 ,  G06V2201/03 ,  G06V2201/07

Abstract: Techniques are for detecting presence of a problematic cellular entity in a target. In an example, using an analysis model, a fluorescence-based image is analyzed. The analysis model is trained using a number of reference fluorescence-based images for detecting the presence of problematic cellular entities in targets. Based on the analysis, a problematic cellular entity present in the target is detected. To perform the detection, the analysis model is trained to differentiate between the fluorescence in the fluorescence-based image emerging from the problematic cellular entity and the fluorescence in the fluorescence-based image emerging from regions other than the problematic cellular entity.

公开(公告)号：US11823386B2

公开(公告)日：2023-11-21

申请号：US17862715

申请日：2022-07-12

Applicant: CANON KABUSHIKI KAISHA

Inventor： Naoki Matsuki ,  Masami Kawagishi ,  Kiyohide Satoh

IPC: G06T7/00 ,  A61B6/03 ,  A61B6/00 ,  G06F18/24 ,  G06F18/214 ,  G06F18/21 ,  G06V10/82

CPC classification number: G06T7/0012 ,  A61B6/032 ,  A61B6/5217 ,  G06F18/214 ,  G06F18/217 ,  G06F18/24 ,  G06V10/82 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06V2201/03

Abstract: Diagnosis is inferred by using at least one of a plurality of inferencers configured to infer diagnosis from a medical image and by using a medical image as an input into the at least one of the plurality of inferencers, and the inferred diagnosis is represented.

公开(公告)号：US11823378B2

公开(公告)日：2023-11-21

申请号：US17107433

申请日：2020-11-30

Applicant: PAIGE.AI, Inc.

Inventor： Patricia Raciti ,  Christopher Kanan ,  Thomas Fuchs ,  Leo Grady

IPC: G06T7/194 ,  G06V10/764 ,  G06T7/00 ,  G06T7/11 ,  G06V10/776 ,  G06V10/82 ,  G06V10/98 ,  G06V20/69

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06T7/194 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V10/993 ,  G06V20/69 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06V2201/03

Abstract: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, detecting one or more image regions from a background of the one or more digital images, determining a prediction, using a machine learning system, of whether at least one first image region of the one or more image regions comprises at least one external contaminant, the machine learning system having been trained using a plurality of training images to predict a presence of external contaminants and/or a location of any external contaminants present in the tissue specimen, and determining, based on the prediction of whether a first image region comprises an external contaminant, whether to process the image region using an processing algorithm.

公开(公告)号：US11819309B2

公开(公告)日：2023-11-21

申请号：US16821877

申请日：2020-03-17

Applicant: LightLab Imaging, Inc.

Inventor： Ajay Gopinath ,  Mark Hoeveler

IPC: G06F3/048 ,  A61B5/00 ,  A61B34/10 ,  A61B34/00 ,  G06T7/62 ,  G06T7/13 ,  G16H30/20 ,  G16H30/40 ,  G16H40/40 ,  G16H40/63 ,  G06F3/0484 ,  G06T7/00 ,  G06V10/764 ,  G06V10/82 ,  A61F2/958

CPC classification number: A61B5/0066 ,  A61B34/10 ,  A61B34/25 ,  G06F3/0484 ,  G06T7/0012 ,  G06T7/13 ,  G06T7/62 ,  G06V10/764 ,  G06V10/82 ,  G16H30/20 ,  G16H30/40 ,  G16H40/40 ,  G16H40/63 ,  A61B2034/107 ,  A61B2034/108 ,  A61F2/958 ,  G06T2200/24 ,  G06T2207/30052 ,  G06T2207/30101 ,  G06V2201/03

Abstract: In part, the disclosure relates to method of displaying a representation of an artery. The method may include storing an intravascular image dataset in a memory device of a diagnostic imaging system, the intravascular image dataset generated in response to intravascular imaging of a segment of an artery; automatically detecting lumen boundary of the segment on a per frame basis; automatically detecting EEL and displaying a stent sizing workflow. In part, the disclosure also relates to automatically detecting one or more regions of calcium relative to lumen boundary of the segment; calculating an angular or circumferential measurement of detected calcium for one or more frames; calculating a calcium thickness of detected calcium for one or more frames; and displaying the calcium thickness and the angular or circumferential measurement of detected calcium for a first frame of the one or more frames.

公开(公告)号：US11813103B2

公开(公告)日：2023-11-14

申请号：US17143192

申请日：2021-01-07

Applicant: SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.

Inventor： Min Ji ,  Weili Peng

IPC: A61B6/00 ,  G06T7/60 ,  G06T7/11 ,  G06T7/13 ,  A61B6/03 ,  G06F18/214

CPC classification number: A61B6/488 ,  A61B6/03 ,  A61B6/544 ,  G06F18/2148 ,  G06T7/11 ,  G06T7/13 ,  G06T7/60 ,  G06T2207/10028 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06V2201/03

Abstract: A method and system for determining a dose of modulation (DOM) profile are provided. The method may include obtaining a 3D image and a topogram image of the object. The method may further include obtaining a dose of modulation (DOM) profile generation model. The DOM profile generation model may be generated by training a preliminary model based on a plurality of sample CT images, a plurality of sample 3D images corresponding to the plurality of sample CT images, respectively, and a plurality of sample topogram images corresponding to the plurality of sample CT images, respectively. The method may further include executing the DOM profile generation model to generate a DOM profile related to a computed tomography (CT) scan of the object based on the 3D image and the topogram image of the object.