公开(公告)号：US12223655B2

公开(公告)日：2025-02-11

申请号：US17569920

申请日：2022-01-06

Applicant: The Trustees of The University of Pennsylvania

Inventor： Jayaram K. Udupa ,  Dewey Odhner ,  Drew A. Torigian ,  Yubing Tong

IPC: G06T7/11 ,  A61N5/10 ,  G06T7/00 ,  G06T7/136 ,  G06T7/143 ,  G06T7/70 ,  G06V10/44

Abstract: A computerized method of providing automatic anatomy recognition (AAR) includes gathering image data from patient image sets, formulating precise definitions of each body region and organ and delineating them following the definitions, building hierarchical fuzzy anatomy models of organs for each body region, recognizing and locating organs in given images by employing the hierarchical models, and delineating the organs following the hierarchy. The method may be applied, for example, to body regions including the thorax, abdomen and neck regions to identify organs.

公开(公告)号：US12190524B2

公开(公告)日：2025-01-07

申请号：US17384299

申请日：2021-07-23

Applicant: DASSAULT SYSTEMES

Inventor： Nicolas Beltrand ,  Mourad Boufarguine ,  Vincent Guitteny

IPC: G06T7/13 ,  G06N3/08 ,  G06T7/143 ,  G06T7/162 ,  G06T7/80 ,  G06T11/20

Abstract: A computer-implemented method for segmenting an object in at least one image acquired by a camera including computing an edge probabilities image based on the image, said edge probabilities image comprising, for each pixel of the image, the probability that said pixel is an edge, computing a segmentation probabilities image based on the image (IM), said segmentation probabilities image comprising, for each pixel of the image (IM), the probability that said pixel belongs to the object (OBJ), and computing a binary mask of the object based on the edge probabilities image and based on the segmentation probabilities image.

公开(公告)号：US20240370998A1

公开(公告)日：2024-11-07

申请号：US18567494

申请日：2022-07-26

Applicant: SHIMADZU CORPORATION

Inventor： Ryuji SAWADA ,  Shuhei YAMAMOTO

IPC: G06T7/00 ,  G06T7/143

Abstract: A cell image analysis method according to this invention includes a step of acquiring a cell image (10) including a cell (90); a step of inputting the cell image to a learned model (6) that has learned classification of the cell into one of two or more types; a step of acquiring an index value (20) indicating accuracy of the classification of the cell that is included in the cell image into one of two or more types based on an analysis result of each of pixels of the cell image output from the learned model; and a step of displaying the acquired index value.

公开(公告)号：US12118704B2

公开(公告)日：2024-10-15

申请号：US17526325

申请日：2021-11-15

Applicant: HON HAI PRECISION INDUSTRY CO., LTD.

Inventor： Chin-Pin Kuo ,  Shih-Chao Chien ,  Tung-Tso Tsai

IPC: G06T9/00 ,  G06T7/00 ,  G06T7/143 ,  G06V10/75

CPC classification number: G06T7/0004 ,  G06T7/143 ,  G06T9/00 ,  G06V10/751 ,  G06T2207/30108

Abstract: A model input size determination method, an electronic device and a storage medium are provided, the method includes acquiring a plurality of test images and a defect result; and encoding each test image to obtain an encoding vector. The encoding vector is decoded to obtain a reconstructed image, then a reconstruction error and a plurality of sub-vectors are calculated; the plurality of sub-vectors is inputted into a Gaussian mixture model, then a plurality of sub-probabilities, an estimated probability and a test error are determined; a detection result in the test image according to the test error and the corresponding error threshold are obtained; an accuracy according to the detection result and the defect result are determined, and an input size is selected from the plurality of preset sizes according to the accuracy. An accuracy of defect detection in manufacturing can be improved.

公开(公告)号：US12100158B2

公开(公告)日：2024-09-24

申请号：US18205353

申请日：2023-06-02

Applicant: Snap Inc.

Inventor： Piers Cowburn ,  David Li ,  Isac Andreas Müller Sandvik ,  Qi Pan ,  Andrew Tristan Spek

IPC: G06T7/11 ,  G06T7/12 ,  G06T7/143 ,  G06T7/174 ,  G06T7/194

CPC classification number: G06T7/11 ,  G06T7/12 ,  G06T7/143 ,  G06T7/174 ,  G06T7/194 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Methods, systems, and devices for generating a persistent world-space ground (or floor) segmentation map (or “texture”) for use in augmented or virtual reality 3D experiences.

公开(公告)号：US12056877B2

公开(公告)日：2024-08-06

申请号：US17594191

申请日：2020-03-25

Applicant: The Johns Hopkins University

Inventor： Yufan He ,  Jerry L. Prince ,  Aaron Carass

IPC: G06K9/00 ,  A61B3/00 ,  A61B3/10 ,  A61B3/12 ,  G06N3/08 ,  G06T7/00 ,  G06T7/11 ,  G06T7/12 ,  G06T7/143 ,  G06T7/73 ,  G16H20/10 ,  G16H70/60

CPC classification number: G06T7/11 ,  A61B3/0025 ,  A61B3/0058 ,  A61B3/1005 ,  A61B3/12 ,  G06N3/08 ,  G06T7/0012 ,  G06T7/12 ,  G06T7/143 ,  G06T7/73 ,  G16H20/10 ,  G16H70/60 ,  G06T2207/10101 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30041

Abstract: A device receives a two-dimensional (2-D) image that depicts a cross-sectional view of a macula comprised of layers and boundaries to segment the layers, and determines spatial coordinates of the 2-D image that include x-coordinates and y-coordinates. The device uses a data model, that has been trained using a deep learning technique, to process the 2-D image and the spatial coordinates to generate boundary maps that indicate likelihoods of voxels of the 2-D image being in positions that are part of particular boundaries. The device determines, by analyzing the boundary maps, an initial set of boundary positions, and determines a final set of boundary positions by using a topological order identification technique to refine the initial set of boundary positions. The device determines the thickness levels of the layers of the macula based on the final set of boundary positions, and performs one or more actions based on the thickness levels.

公开(公告)号：US12056211B2

公开(公告)日：2024-08-06

申请号：US17501899

申请日：2021-10-14

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Yifan Hu ,  Yuexiang Li ,  Yefeng Zheng

IPC: G06V20/70 ,  A61B6/00 ,  G06F18/21 ,  G06F18/214 ,  G06N3/0455 ,  G06T7/10 ,  G06T7/143 ,  G06T9/00 ,  G06V20/69

CPC classification number: G06F18/2155 ,  A61B6/5294 ,  G06F18/2178 ,  G06N3/0455 ,  G06T7/10 ,  G06T7/143 ,  G06T9/002 ,  G06V20/695 ,  G06V20/70 ,  G06T2207/20112 ,  G06T2207/30004 ,  G06T2219/004

Abstract: A method for determining a target image to be labeled includes: obtaining an original image and an autoencoder (AE) set, the original image being an image having not been labeled, the AE set including N AEs; obtaining an encoded image set corresponding to the original image by using the AE set, the encoded image set including N encoded images, the encoded images being corresponding to the AEs; obtaining the encoded image set and a segmentation result set corresponding to the original image by using an image segmentation network, the image segmentation network including M image segmentation sub-networks, and the segmentation result set including [(N+1)*M] segmentation results; determining labeling uncertainty corresponding to the original image according to the segmentation result set; and determining whether the original image is a target image according to the labeling uncertainty.

公开(公告)号：US12039788B2

公开(公告)日：2024-07-16

申请号：US17584879

申请日：2022-01-26

Applicant: NATIONAL YANG MING CHIAO TUNG UNIVERSITY

Inventor： Jiun-In Guo ,  Jen-Shuo Chang

IPC: G01S17/89 ,  G06T7/143 ,  G06V10/762 ,  G06V20/56

CPC classification number: G06V20/588 ,  G01S17/89 ,  G06T7/143 ,  G06V10/763 ,  G06T2207/20221

Abstract: A path planning system includes an image-capturing device, a point-cloud map-retrieving device, and a processing device. The image-capturing device captures a first and a second camera road image. The point-cloud map-retrieving device retrieves distance data points to create a road distance point-cloud map. The processing device receives the road distance point-cloud map and the first and second camera road images, calibrates and fuses those to generate a road camera point-cloud fusion map, and then determines the road-line information of the second camera road image to generate a road-segmented map. The road-segmented map and the road camera point-cloud fusion map are fused. The distance data of the road-segmented map are obtained according to distance data points. A front driving path for the target road is planned according to the distance data.

公开(公告)号：US11990224B2

公开(公告)日：2024-05-21

申请号：US17214442

申请日：2021-03-26

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Hamid Jafarkhani ,  Saeed Karimi-Bidhendi ,  Arash Kheradvar

IPC: G06T7/143 ,  G06F18/21 ,  G06F18/214 ,  G06N3/08 ,  G06N7/01 ,  G06T7/00 ,  G06V10/82 ,  G16H30/40

CPC classification number: G16H30/40 ,  G06F18/2148 ,  G06F18/2185 ,  G06F18/2193 ,  G06N3/08 ,  G06N7/01 ,  G06T7/0012 ,  G06T7/143 ,  G06V10/82 ,  G06T2207/10088 ,  G06T2207/20084 ,  G06T2207/30048 ,  G06V2201/031

Abstract: Methods, devices, and systems that are related to facilitating an automated, fast and accurate model for cardiac image segmentation, particularly for image data of children with complex congenital heart disease are disclosed. In one example aspect, a generative adversarial network is disclosed. The generative adversarial network includes a generator configured to generate synthetic imaging samples associated with a cardiovascular system, and a discriminator configured to receive the synthetic imaging samples from the generator and determine probabilities indicating likelihood of the synthetic imaging samples corresponding to real cardiovascular imaging sample. The discriminator is further configured to provide the probabilities determined by the discriminator to the generator and the discriminator to allow the parameters of the generator and the parameters of the discriminator to be adjusted iteratively until an equilibrium between the generator and the discriminator is established.

公开(公告)号：US11983885B2

公开(公告)日：2024-05-14

申请号：US17540232

申请日：2021-12-02

Applicant: Wenzhou University

Inventor： Pengjun Wang ,  Songwei Zhao ,  Huiling Chen ,  Suling Xu ,  Wenming He ,  Gang Li

IPC: G06T7/143 ,  G06T7/00

CPC classification number: G06T7/143 ,  G06T7/0012

Abstract: The invention discloses a multi-threshold segmentation method for medical images based on an improved salp swarm algorithm. A two-dimensional histogram is established by means of a grayscale image of a medical image and a non-local mean image, then a salp swarm algorithm is used to determine thresholds selected by a Kapur entropy-based threshold method, and the salp swarm algorithm is improved and mutated by an individual-linked mutation strategy during the threshold selection process to avoid local optimization, so that the segmentation effect on the medical image is optimized; and the method has the advantages of good robustness and high accuracy.