公开(公告)号：US12131473B2

公开(公告)日：2024-10-29

申请号：US18523098

申请日：2023-11-29

Applicant: PAIGE.AI, Inc.

Inventor： Rodrigo Ceballos Lentini ,  Christopher Kanan ,  Patricia Raciti ,  Leo Grady ,  Thomas Fuchs

IPC: G06T7/00 ,  G06F18/214 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06F18/214 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.

公开(公告)号：US20240350106A1

公开(公告)日：2024-10-24

申请号：US18684813

申请日：2022-08-17

Applicant: University of Virginia Patent Foundation

Inventor： Bijoy Kundu ,  Yinlin Li ,  Carlos Leiva-Salinas ,  Robert S. Shetlick

IPC: A61B6/00 ,  A61B6/03 ,  A61B6/50 ,  G01R33/48 ,  G06T7/00 ,  G06T7/11 ,  G06T7/30 ,  G06V10/764 ,  G06V10/766

CPC classification number: A61B6/5217 ,  A61B6/037 ,  A61B6/507 ,  A61B6/5247 ,  G01R33/481 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/30 ,  G06V10/764 ,  G06V10/766 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/20064 ,  G06T2207/20081 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Methods, systems, and computer readable media for performing FDG positron emission tomography (PET) quantification, segmentation, and classification of abnormalities are disclosed. One method includes receiving a plurality of magnetic resonance (MR) images corresponding to a target site of a subject and generating three dimensional (3D) area masks of abnormality volumes from the plurality of MR images. The method further includes segmenting the 3D area masks into one or more individual seed images for each of the abnormality volumes and overlaying the one or more individual seed images onto co-registered parametric PET maps to generate kinetic rate parameters for each of the abnormality volumes. The method also includes utilizing the kinetic rate parameters to train a logistic regression engine to predict a target site condition assessment based on a classification of the abnormality volumes.

公开(公告)号：US12121328B2

公开(公告)日：2024-10-22

申请号：US12339092

申请日：2008-12-19

Applicant: Jeffrey David Mullen ,  Will Reutzel

Inventor： Jeffrey David Mullen ,  Will Reutzel

IPC: G06K19/07 ,  A61B5/02 ,  G06F3/0488 ,  G06K7/00 ,  G06K7/08 ,  G06K7/10 ,  G06K19/06 ,  G06K19/073 ,  G06K19/077 ,  G06K19/08 ,  G06Q20/18 ,  G06Q20/20 ,  G06Q20/34 ,  G06Q20/38 ,  G06Q20/40 ,  G06Q30/0207 ,  G06Q30/0241 ,  G06Q30/0601 ,  G06T7/62 ,  G06V10/24 ,  G06V10/25 ,  G07F7/08 ,  G07F7/10

CPC classification number: A61B5/02042 ,  A61B5/02 ,  G06F3/0488 ,  G06K7/0004 ,  G06K7/084 ,  G06K7/087 ,  G06K7/10297 ,  G06K19/06187 ,  G06K19/06206 ,  G06K19/07 ,  G06K19/0702 ,  G06K19/0704 ,  G06K19/0723 ,  G06K19/0725 ,  G06K19/07345 ,  G06K19/07703 ,  G06K19/07705 ,  G06K19/07707 ,  G06K19/07709 ,  G06K19/07749 ,  G06K19/0775 ,  G06K19/07766 ,  G06K19/07769 ,  G06K19/07773 ,  G06K19/083 ,  G06Q20/18 ,  G06Q20/20 ,  G06Q20/34 ,  G06Q20/341 ,  G06Q20/3415 ,  G06Q20/352 ,  G06Q20/385 ,  G06Q20/401 ,  G06Q30/0222 ,  G06Q30/0241 ,  G06Q30/0277 ,  G06Q30/0641 ,  G06T7/62 ,  G06V10/24 ,  G06V10/25 ,  G07F7/0806 ,  G07F7/1008 ,  G06T2207/10024 ,  G06T2207/30004 ,  G06V2201/03

Abstract: A card is provided, such as a credit card or security card, that may transmit information to a magnetic stripe reader via a magnetic emulator. The emulator may transmit the information in order to reduce the amount of circuitry needed to emulate a particular block of information. Additionally, for example, one or more buttons may be included on the card. Buttons may be includes, for example, to provide a control interface to navigate through various options of the card. Additionally, coding schemes may be selected via buttons. Furthermore, a card may be locked until a private number is entered into a card or a number may only be generated (e.g., displayed and/or emulated) once a particular private number is entered into a card.

公开(公告)号：US12118728B2

公开(公告)日：2024-10-15

申请号：US17648686

申请日：2022-01-24

Applicant: Siemens Healthineers AG

Inventor： Xiaodong Zhong ,  Vibhas S. Deshpande ,  Marcel Dominik Nickel ,  Stephan Kannengiesser

IPC: G06T7/00 ,  G06V10/36 ,  G06V10/75 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06T7/0016 ,  G06V10/36 ,  G06V10/751 ,  G06V10/7747 ,  G06V10/82 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06V2201/03

Abstract: A computer implemented method of processing a medical image is disclosed. The method includes receiving a medical image comprising a first plurality of pixels each having an initial pixel value. For each of the first plurality of pixels, a filtering operation is applied to the pixel to generate a filtered pixel value for the pixel based on the initial pixel values of pixels that surround the pixel in the medical image. For each of the first plurality of pixels, a comparison of the initial pixel value with the filtered pixel value is performed. The method comprises, for each of the first plurality of pixels, determining, based on the comparison, whether or not to categorize the pixel as an erroneous pixel; and for each of the first plurality of pixels for which it is determined to categorize the pixel as an erroneous pixel, categorizing the pixel as an erroneous pixel.

公开(公告)号：US20240339195A1

公开(公告)日：2024-10-10

申请号：US18749314

申请日：2024-06-20

Applicant: Howmedica Osteonics Corp.

Inventor： Yannick Morvan ,  Sergii Poltaretskyi ,  Jean Chaoui ,  Damien Cariou

IPC: G16H20/40 ,  A61B5/00 ,  A61B5/11 ,  A61B17/00 ,  A61B17/14 ,  A61B17/15 ,  A61B17/16 ,  A61B17/17 ,  A61B34/00 ,  A61B34/10 ,  A61B34/20 ,  A61B90/00 ,  A61B90/50 ,  A61B90/92 ,  A61F2/40 ,  A61F2/46 ,  G02B27/00 ,  G02B27/01 ,  G06F3/01 ,  G06F3/04815 ,  G06F3/0482 ,  G06F3/0483 ,  G06F30/10 ,  G06F30/20 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11 ,  G06T7/55 ,  G06T11/00 ,  G06T19/00 ,  G06T19/20 ,  G09B5/06 ,  G09B9/00 ,  G09B19/00 ,  G09B23/28 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H40/60 ,  G16H40/63 ,  G16H40/67 ,  G16H50/20 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/20 ,  G16H70/60 ,  G16H80/00 ,  H04N13/122 ,  H04N13/332

CPC classification number: G16H20/40 ,  A61B5/1114 ,  A61B5/1121 ,  A61B5/1127 ,  A61B5/681 ,  A61B17/142 ,  A61B17/1604 ,  A61B17/1626 ,  A61B17/1659 ,  A61B17/1684 ,  A61B17/1703 ,  A61B34/10 ,  A61B34/25 ,  A61B90/08 ,  A61B90/36 ,  A61B90/361 ,  A61B90/37 ,  A61B90/39 ,  A61B90/92 ,  A61F2/40 ,  A61F2/4081 ,  G02B27/0075 ,  G02B27/017 ,  G02B27/0172 ,  G06F3/011 ,  G06F3/04815 ,  G06F3/0482 ,  G06F30/10 ,  G06F30/20 ,  G06N3/08 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/55 ,  G06T11/00 ,  G06T19/006 ,  G06T19/20 ,  G09B5/06 ,  G09B9/00 ,  G09B19/003 ,  G09B23/28 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H40/60 ,  G16H40/63 ,  G16H40/67 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/20 ,  G16H70/60 ,  G16H80/00 ,  H04N13/122 ,  H04N13/332 ,  A61B5/744 ,  A61B2017/00115 ,  A61B2017/00119 ,  A61B2017/00123 ,  A61B17/151 ,  A61B17/1775 ,  A61B17/1778 ,  A61B2034/102 ,  A61B2034/104 ,  A61B2034/105 ,  A61B2034/107 ,  A61B2034/108 ,  A61B2034/2048 ,  A61B2034/2051 ,  A61B2034/2055 ,  A61B2034/2065 ,  A61B2034/2068 ,  A61B2034/252 ,  A61B2034/254 ,  A61B2090/062 ,  A61B2090/067 ,  A61B2090/0801 ,  A61B2090/0807 ,  A61B2090/365 ,  A61B2090/366 ,  A61B2090/367 ,  A61B2090/368 ,  A61B2090/373 ,  A61B2090/374 ,  A61B2090/3762 ,  A61B2090/378 ,  A61B2090/3937 ,  A61B2090/3945 ,  A61B2090/397 ,  A61B2090/502 ,  A61B2505/05 ,  A61B2562/0219 ,  A61F2002/4011 ,  A61F2/4606 ,  A61F2/4612 ,  A61F2002/4633 ,  A61F2002/4658 ,  A61F2002/4668 ,  G02B2027/0141 ,  G02B2027/0174 ,  G06F3/0483 ,  G06N3/04 ,  G06T2200/24 ,  G06T2207/10016 ,  G06T2207/20036 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30008 ,  G06T2207/30052 ,  G06T2207/30204 ,  G06T2210/41 ,  G06T2219/2004 ,  G06V2201/03 ,  G16H50/20

Abstract: A computing system obtains an information model specifying a first surgical plan for an orthopedic surgery to be performed on a patient. Additionally, the computing system modifies the first surgical plan during an intraoperative phase of the orthopedic surgery to generate a second surgical plan. During the intraoperative phase of the orthopedic surgery, a visualization device may present a visualization for display that is based on the second surgical plan.

公开(公告)号：US20240338932A1

公开(公告)日：2024-10-10

申请号：US18627810

申请日：2024-04-05

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Mohammad Reza Hosseinzadeh Taher ,  Jianming Liang

IPC: G06V10/774 ,  G06V10/26 ,  G06V10/82

CPC classification number: G06V10/774 ,  G06V10/26 ,  G06V10/82 ,  G06V2201/03

Abstract: A self-supervised learning (SSL) model that learns from human anatomy in a plurality of medical images. A system receives a plurality of medical images and selects one for processing, including dividing the human anatomy in the selected medical image into a plurality of parts via an Anatomy Decomposer (AD) module. The AD module receives the selected medical image, generates a random anchor instance that represents a selected one of a plurality of parts of the selected medical image, and generates embedding vectors based the random anchor instance. In on embodiment, the AD module augments the random anchor instance to obtain two views of the selected part, which are passed to a respective pair of encoders that generate a respective embedding vector based thereon.

公开(公告)号：US12112476B2

公开(公告)日：2024-10-08

申请号：US17674818

申请日：2022-02-17

Applicant: FUJIFILM Corporation

Inventor： Naoki Matsuura

IPC: G06T7/00 ,  G06T7/70 ,  G06V10/764 ,  G08B21/18

CPC classification number: G06T7/0012 ,  G06T7/70 ,  G06V10/764 ,  G06T2207/30004 ,  G06V2201/03 ,  G08B21/18

Abstract: A medical support device includes a processor, in which the processor acquires a medical image, acquires correspondence information in which the medical image is associated in advance with presence or absence of a foreign substance image included in the medical image, generates foreign substance estimation information obtained by estimating the presence or absence of the foreign substance image included in the medical image based on the medical image and the correspondence information, and controls a notification based on the foreign substance estimation information.

公开(公告)号：US20240331412A1

公开(公告)日：2024-10-03

申请号：US18580508

申请日：2022-07-18

Applicant: Bayer Aktiengesellschaft

Inventor： Gerard SANROMA GÜELL ,  Markus BLANK ,  Mark Alexander KLEMENS

IPC: G06V20/64 ,  G06V10/774 ,  G06V10/776 ,  G06V40/10

CPC classification number: G06V20/64 ,  G06V10/774 ,  G06V10/776 ,  G06V40/10 ,  G06V2201/03

Abstract: The present invention relates to the technical field of automatically determining the content of images. In particular, the present invention relates to a process for assigning images to predefined categories depending on their content. Subject matter of the present invention is a computer-implemented method of automatically determining to which part of an object the content depicted in one or more images belongs to, a computer system configured to execute the computer-implemented method, and a non-transitory computer-readable storage medium comprising processor-executable instructions with which to perform the computer-implemented method.

公开(公告)号：US20240324963A1

公开(公告)日：2024-10-03

申请号：US18616747

申请日：2024-03-26

Applicant: Siemens Healthineers AG

Inventor： Manasi DATAR

IPC: A61B5/00 ,  G06V10/25

CPC classification number: A61B5/7264 ,  G06V10/25 ,  G06V2201/03

Abstract: Various examples of the disclosure pertain to the characterization of one or more metastases using a neural network, as well as an apparent diffusion coefficient, ADC, map as obtained from diffusion-weighted magnetic resonance imaging, DWI MRI. A convolutional neural network can be employed. Training processes and inference processes are disclosed.

公开(公告)号：US12106394B2

公开(公告)日：2024-10-01

申请号：US17379036

申请日：2021-07-19

Applicant: FUJIFILM Corporation

Inventor： Masaaki Oosake ,  Shumpei Kamon

IPC: G06T11/00 ,  A61B1/00 ,  G06F3/14 ,  G06F18/24 ,  G06T3/40 ,  G06T7/00 ,  G06T11/60 ,  G06V10/25 ,  G06V10/82 ,  G06V20/20

CPC classification number: G06T11/00 ,  A61B1/000094 ,  A61B1/00045 ,  G06F3/14 ,  G06F18/24 ,  G06T3/40 ,  G06T7/0012 ,  G06T11/60 ,  G06V10/25 ,  G06V10/82 ,  G06V20/20 ,  G06T2207/10068 ,  G06T2207/30004 ,  G06V2201/03

Abstract: A medical image processing apparatus includes a recognition unit that detects a region of interest from a medical image and classifies the region of interest, a display control unit that causes emphasis display of the region of interest and a classification for the region of interest to be displayed in a screen for displaying the observation target, and a change determining unit that determines at least any of whether to change a display style of the emphasis display or whether to change a display style of the classification. The display control unit changes the display style of the emphasis display in the case where it is determined to change the display style of the emphasis display and changes the display style of the classification in the case where it is determined to change the display style of the classification.