公开(公告)号：US11985449B2

公开(公告)日：2024-05-14

申请号：US18169150

申请日：2023-02-14

Applicant: FUJIFILM Corporation

Inventor： Masaaki Oosake

IPC: G06T7/00 ,  A61B1/00 ,  G06T7/60 ,  G06T7/70 ,  G06T11/00 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  H04N7/18 ,  H04N23/50

CPC classification number: H04N7/183 ,  A61B1/00006 ,  A61B1/000094 ,  A61B1/0005 ,  G06T7/0012 ,  G06T7/60 ,  G06T7/70 ,  G06T11/001 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  H04N23/50 ,  G06T2207/10068 ,  G06T2207/30004 ,  G06V2201/03 ,  H04N23/555

Abstract: Provided are a medical image processing device, a medical image processing method, and an endoscope system that make it easy to compare a region of interest and its peripheral region with each other and make it unlikely to miss the region of interest if the region of interest in a time-series image is reported by using figures. A coordinates calculating unit (43) that calculates, on the basis of region-of-interest information indicating a region of interest in a time-series image, a plurality of sets of coordinates of interest on an outline of a polygon or circle having a symmetric shape that surrounds the region of interest. A reporting information display control unit (45B) that superposes figures on the basis of the calculated plurality of sets of coordinates of interest when superposing the figures for reporting the region of interest on the time-series image. Herein, the figures have a size that does not change with respect to a size of the region of interest.

公开(公告)号：US20240153269A1

公开(公告)日：2024-05-09

申请号：US18281995

申请日：2022-04-14

Applicant: DIGITAL SURGERY LIMITED

Inventor： Carole RJ Addis ,  Sheldon K. Hall ,  Pinja ME Haikka ,  George Bruce Murgatroyd

IPC: G06V20/40 ,  G06T11/20 ,  G06V10/74 ,  G06V10/762 ,  G06V20/70 ,  G16H20/40 ,  G16H50/70 ,  G16H70/20

CPC classification number: G06V20/41 ,  G06T11/206 ,  G06V10/761 ,  G06V10/762 ,  G06V20/49 ,  G06V20/70 ,  G16H20/40 ,  G16H50/70 ,  G16H70/20 ,  G06T2200/24 ,  G06V2201/03

Abstract: An aspect includes a computer-implemented method that identifies variations in surgical approaches to medical procedures. Surgical videos documenting multiple cases of a medical procedure are analyzed to identify different surgical approaches used by service providers when performing the medical procedure. According to some aspects surgical phases are identified in each surgical video and groups of similar surgical phase sequences are grouped into surgical approaches.

公开(公告)号：US20240153090A1

公开(公告)日：2024-05-09

申请号：US18279497

申请日：2021-03-01

Applicant: NEC Corporation

Inventor： Masahiro SAIKOU

IPC: G06T7/00 ,  A61B1/00 ,  G06T7/11 ,  G06V10/40 ,  G06V10/74 ,  G06V10/764 ,  G16H30/20 ,  G16H30/40

CPC classification number: G06T7/0016 ,  A61B1/000094 ,  G06T7/11 ,  G06V10/40 ,  G06V10/761 ,  G06V10/764 ,  G16H30/20 ,  G16H30/40 ,  G06T2207/10016 ,  G06T2207/10068 ,  G06T2207/20084 ,  G06T2207/30096 ,  G06V2201/03

Abstract: The image processing device 1X includes a classification means 31X, an image selection means 33X, and a region extraction means 34X. The classification means 31X classifies each captured image acquired in time series by photographing an inspection target by a photographing unit provided in an endoscope, according to whether or not the each captured image includes an attention part to be paid attention to. The image selection means 33X selects a target image to be subjected to extraction of a region of the attention part from the each captured image, based on a result of the classification. The region extraction means 34X extracts the area of the attention part from the target image.

公开(公告)号：US20240144679A1

公开(公告)日：2024-05-02

申请号：US18496741

申请日：2023-10-27

Applicant: Verb Surgical Inc.

Inventor： Bokai Zhang

IPC: G06V20/40 ,  G06V10/82

CPC classification number: G06V20/41 ,  G06V10/82 ,  G06V20/46 ,  G06V20/49 ,  G06V2201/03

Abstract: A Cross-Enhancement Causal Transformer or simply a Cross-Enhancement Transformer (C-ECT) is described as a modification of previous transformer architectures that is suitable for online surgical phase recognition. Additionally, a Cross-Attention Feature Fusion (CAFF) is described that better integrates the global and location information in the C-ECT. This can achieve better performance on the Cholec80 dataset than the current state-of-the-art methods in accuracy and precision, recall, and in the Jaccard score. Other aspects are also described and claimed.

公开(公告)号：US11972620B2

公开(公告)日：2024-04-30

申请号：US17076008

申请日：2020-10-21

Applicant: Cytek Biosciences, Inc.

Inventor： Alan Li ,  Shobana Vaidyanathan

IPC: G06V20/69 ,  G01N15/14 ,  G06T7/00 ,  G06T7/11

CPC classification number: G06V20/698 ,  G01N15/1475 ,  G06T7/0012 ,  G06T7/11 ,  G06V20/693 ,  G06V20/695 ,  G06T2207/10064 ,  G06T2207/30024 ,  G06V2201/03

Abstract: A classifier engine provides cell morphology identification and cell classification in computer-automated systems, methods and diagnostic tools. The classifier engine performs multispectral segmentation of thousands of cellular images acquired by a multispectral imaging flow cytometer. As a function of imaging mode, different ones of the images provide different segmentation masks for cells and subcellular parts. Using the segmentation masks, the classifier engine iteratively optimizes model fitting of different cellular parts. The resulting improved image data has increased accuracy of location of cell parts in an image and enables detection of complex cell morphologies in the image. The classifier engine provides automated ranking and selection of most discriminative shape based features for classifying cell types.

公开(公告)号：US11972571B2

公开(公告)日：2024-04-30

申请号：US17497954

申请日：2021-10-10

Applicant: Infervision Medical Technology Co., Ltd.

Inventor： Enyou Liu ,  Shaokang Wang ,  Kuan Chen

IPC: G06T7/11 ,  G06F18/21 ,  G06F18/213 ,  G06F18/25 ,  G06N3/045 ,  G06T5/70 ,  G06T7/187 ,  G06T7/194

CPC classification number: G06T7/11 ,  G06F18/213 ,  G06F18/217 ,  G06F18/253 ,  G06N3/045 ,  G06T5/70 ,  G06T7/187 ,  G06T7/194 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/30101 ,  G06V2201/03

Abstract: The method for image segmentation includes: acquiring, according to an image to be segmented including a background, a mediastinum, an artery and a vein, a first segmentation result of the mediastinum, the artery, the vein and the background in a mediastinum region of the image to be segmented; acquiring, according to the image to be segmented, a second segmentation result of a blood vessel and the background in an epitaxial region of the image to be segmented; and acquiring, according to the first segmentation result and the second segmentation result, a segmentation result of the mediastinum, the artery, the vein and the background of the image to be segmented, so that the segmentation accuracy and the segmentation efficiency of the artery and the vein may be improved.

公开(公告)号：US20240135603A1

公开(公告)日：2024-04-25

申请号：US18546376

申请日：2022-02-14

Applicant: Mayo Foundation for Medical Education and Research

Inventor： Christopher P. Favazza ,  Andrea Ferrero ,  Liqiang Ren

IPC: G06T11/00 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06T11/005 ,  G06V10/774 ,  G06V10/82 ,  G06T2211/441 ,  G06T2211/448 ,  G06V2201/03

Abstract: Metal artifacts are reduced in x-ray computed tomography (“CT”) images using a suitably trained neural network, such as a convolutional neural network (“CNN”). Virtual metal DATA objects are inserted to either the raw projection data or CT image data (e.g., from pre-procedural CT scans) to generate sets of matching artifact-corrupted and artifact-uncorrupted images, and a CNN, or other neural network, is trained to separate the contribution to each image pixel due to patient anatomy, metal object, or metal object-induced artifact. The contributions from metal object-induced artifacts can then be removed to generate a final, artifact-reduced image.

公开(公告)号：US11967412B2

公开(公告)日：2024-04-23

申请号：US17867876

申请日：2022-07-19

Applicant: HEALTHY.IO LTD.

Inventor： Yonatan Adiri ,  Ron Zohar ,  Paula LeClair

IPC: G06K9/00 ,  A61B5/00 ,  A61B34/10 ,  G02B27/01 ,  G06T7/00 ,  G06T7/50 ,  G06T7/62 ,  G06T7/70 ,  G06T7/80 ,  G06T7/90 ,  G06T11/00 ,  G06T11/20 ,  G06T15/20 ,  G06V10/25 ,  G06V10/60 ,  G06V10/94 ,  G06V20/00 ,  G16H20/10 ,  G16H30/20 ,  G16H30/40 ,  G16H40/60 ,  G16H40/67 ,  G16H50/50 ,  G16H80/00 ,  H04N5/272 ,  H04N23/55 ,  H04N23/60 ,  H04N23/63 ,  H04M1/72403

CPC classification number: G16H30/20 ,  A61B5/0013 ,  A61B5/0015 ,  A61B5/0077 ,  A61B5/445 ,  A61B5/6898 ,  A61B34/10 ,  G02B27/0101 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/0016 ,  G06T7/50 ,  G06T7/62 ,  G06T7/70 ,  G06T7/80 ,  G06T7/90 ,  G06T11/00 ,  G06T11/001 ,  G06T11/203 ,  G06T15/205 ,  G06V10/25 ,  G06V10/60 ,  G06V10/94 ,  G06V20/00 ,  G16H20/10 ,  G16H30/40 ,  G16H40/60 ,  G16H40/67 ,  G16H50/50 ,  G16H80/00 ,  H04N5/272 ,  H04N23/55 ,  H04N23/635 ,  H04N23/64 ,  A61B2034/101 ,  A61B2034/107 ,  A61B2560/0223 ,  A61B2560/0233 ,  A61B2560/0276 ,  A61B2560/0431 ,  A61B2560/0487 ,  G02B2027/0138 ,  G02B2027/014 ,  G06T2200/24 ,  G06T2207/10016 ,  G06T2207/10028 ,  G06T2207/10048 ,  G06T2207/20212 ,  G06T2207/30004 ,  G06T2207/30088 ,  G06T2207/30244 ,  G06T2210/41 ,  G06V2201/03 ,  G06V2201/034 ,  H04M1/72403

Abstract: Embodiments consistent with the present disclosure provide systems, methods, and devices for providing wound capturing guidance. In one example, consistent with the disclosed embodiments, an example system may: display, on a mobile device, a user interface configured to guide a patient through one or more steps for performing a medical action, the plurality of steps including at least: using at least one item of a medical kit; and capturing at least one image of at least part of the at least one item of the medical kit using at least one image sensor associated with the mobile device. The example system may also: detect a failure to successfully complete the medical action; select from one or more alternative reactions, a reaction to the detected failure likely to bring a successful completion of the medical action; and provide instructions associated with the selected reaction.

公开(公告)号：US11967127B2

公开(公告)日：2024-04-23

申请号：US16382705

申请日：2019-04-12

Applicant: Sony Interactive Entertainment Inc.

Inventor： Komath Naveen Kumar

IPC: G06V10/44 ,  G06F18/21 ,  G06F18/22 ,  G06F18/24 ,  G06T7/00 ,  G06V10/776 ,  G06V10/82 ,  G06V40/16

CPC classification number: G06V10/454 ,  G06F18/217 ,  G06F18/22 ,  G06F18/24 ,  G06T7/0016 ,  G06V10/776 ,  G06V10/82 ,  G06V40/176 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06V2201/03

Abstract: Systems and methods capture image dynamics and use those captured image dynamics for image feature recognition and classification. Other methods and systems train a neural network to capture image dynamics. An image vector representing image dynamics is extracted from an image of an image stream using a first neural network. A second neural network, predicts a previous and/or subsequent image in the image stream from the image vector. The predicted previous and/or subsequent image is compared with an actual previous and/or subsequent image from the image stream. The first and second neural networks are trained using the result of the comparison.

公开(公告)号：US20240127611A1

公开(公告)日：2024-04-18

申请号：US18554201

申请日：2022-04-08

Applicant: Coriolis Pharma Research GmbH

Inventor： Andrea Hawe ,  Tim Menzen ,  Adam Grabarek ,  Alexandra Roesch

IPC: G06V20/69 ,  G01N1/30 ,  G01N15/01 ,  G01N15/10 ,  G01N15/1433 ,  G06V10/774 ,  G06V10/82 ,  G06V20/70

CPC classification number: G06V20/698 ,  G01N1/30 ,  G01N15/01 ,  G01N15/1023 ,  G01N15/1433 ,  G06V10/774 ,  G06V10/82 ,  G06V20/70 ,  G01N2015/1006 ,  G06V2201/03

Abstract: The disclosure presented herein provides methods for quantifying viable cells and particulate cell impurities in a cell-based product sample. The method is implemented on a convolutional neural network (CNN) that learns to classify flow-imaging microscopy (FIM) images. The CNN learning is accomplished by using a training set of classified images of viable cells and different types of impurities.