公开(公告)号：US11581087B2

公开(公告)日：2023-02-14

申请号：US17064680

申请日：2020-10-07

Applicant: GE Precision Healthcare LLC

Inventor： Laszlo Rusko ,  Elisabetta Grecchi ,  Petra Takacs

IPC: G06T7/00 ,  G16H30/40 ,  G06T7/10 ,  G06N3/08

Abstract: A method, a system and a computer readable medium for automatic segmentation of a 3D medical image, the 3D medical image comprising an object to be segmented, the method characterized by comprising: carrying out, by using a machine learning model, in at least two of a first, a second and a third orthogonal orientation, 2D segmentations for the object in slices of the 3D medical image to derive 2D segmentation data; determining a location of a bounding box (10) within the 3D medical image based on the 2D segmentation data, the bounding box (10) having predetermined dimensions; and carrying out a 3D segmentation for the object in the part of the 3D medical image corresponding to the bounding box (10).

公开(公告)号：US20230386022A1

公开(公告)日：2023-11-30

申请号：US17664702

申请日：2022-05-24

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Hongxiang Yi ,  Rakesh Mullick ,  Lehel Mihály Ferenczi ,  Gopal Biligeri Avinash ,  Borbála Deák-Karancsi ,  Balázs Péter Cziria ,  Laszlo Rusko

IPC: G06T7/00 ,  A61B8/08 ,  G06T7/149 ,  G06T7/174

CPC classification number: G06T7/0012 ,  A61B8/0883 ,  G06T7/149 ,  G06T7/174 ,  G06T2207/10136 ,  G06T2207/20061 ,  G06T2207/20124 ,  G06T2207/30048

Abstract: Techniques are described that facilitate dynamic multimodal segmentation selection and fusion in medical imaging. In one example embodiment, a computer processing system receives a segmentation dataset comprising a combination of different image segmentations of an anatomical object of interest respectively segmented via different segmentation models from different medical images captured of the (same) anatomical object, wherein the different medical images and the different image segmentations vary with respect to at least one of, capture modality, acquisition protocol, or acquisition parameters. The system employs a dynamic ranking protocol as opposed to a static ranking protocol to determine ranking scores for the different image segmentations that control relative contributions of the different image segmentations in association with combining the different image segmentations into a fused segmentation for the anatomical object. The system further combines the different image segmentations based on the ranking scores to generate the fused image segmentation.

公开(公告)号：US12249067B2

公开(公告)日：2025-03-11

申请号：US17664702

申请日：2022-05-24

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Hongxiang Yi ,  Rakesh Mullick ,  Lehel Mihály Ferenczi ,  Gopal Biligeri Avinash ,  Borbála Deák-Karancsi ,  Balázs Péter Cziria ,  Laszlo Rusko

IPC: G06T7/00 ,  A61B8/08 ,  G06T7/149 ,  G06T7/174

Abstract: Techniques are described that facilitate dynamic multimodal segmentation selection and fusion in medical imaging. In one example embodiment, a computer processing system receives a segmentation dataset comprising a combination of different image segmentations of an anatomical object of interest respectively segmented via different segmentation models from different medical images captured of the (same) anatomical object, wherein the different medical images and the different image segmentations vary with respect to at least one of, capture modality, acquisition protocol, or acquisition parameters. The system employs a dynamic ranking protocol as opposed to a static ranking protocol to determine ranking scores for the different image segmentations that control relative contributions of the different image segmentations in association with combining the different image segmentations into a fused segmentation for the anatomical object. The system further combines the different image segmentations based on the ranking scores to generate the fused image segmentation.

公开(公告)号：US20240127047A1

公开(公告)日：2024-04-18

申请号：US18046347

申请日：2022-10-13

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Hongxu Yang ,  Gopal Biligeri Avinash ,  Balázs Péter Cziria ,  Pál Tegzes ,  Xiaomeng Dong ,  Ravi Soni ,  Lehel Mihály Ferenczi ,  Laszlo Rusko

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/0454

Abstract: Systems/techniques that facilitate deep learning image analysis with increased modularity and reduced footprint are provided. In various embodiments, a system can access medical imaging data. In various aspects, the system can perform, via execution of a deep learning neural network, a plurality of inferencing tasks on the medical imaging data. In various instances, the deep learning neural network can comprise a common backbone in parallel with a plurality of task-specific backbones. In various cases, the plurality of task-specific backbones can respectively correspond to the plurality of inferencing tasks.

公开(公告)号：US20210125707A1

公开(公告)日：2021-04-29

申请号：US17064680

申请日：2020-10-07

Applicant: GE Precision Healthcare LLC

Inventor： Laszlo Rusko ,  Elisabetta Grecchi ,  Petra Takacs

IPC: G16H30/40 ,  G06T7/00 ,  G06T7/10 ,  G06N3/08

Abstract: A method, a system and a computer readable medium for automatic segmentation of a 3D medical image, the 3D medical image comprising an object to be segmented, the method characterized by comprising: carrying out, by using a machine learning model, in at least two of a first, a second and a third orthogonal orientation, 2D segmentations for the object in slices of the 3D medical image to derive 2D segmentation data; determining a location of a bounding box (10) within the 3D medical image based on the 2D segmentation data, the bounding box (10) having predetermined dimensions; and carrying out a 3D segmentation for the object in the part of the 3D medical image corresponding to the bounding box (10).