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公开(公告)号：US12229953B2
公开(公告)日：2025-02-18
申请号：US17951281
申请日：2022-09-23
Applicant: GE Precision Healthcare LLC
Inventor： Pal Tegzes , Zita Herczeg , Tao Tan , Balazs Peter Cziria , Alec Joseph Baenen , Gireesha Chintharnani Rao , Lehel Ferenczi , Gopal Biligeri Avinash , Zoltan Kiss , Hongxu Yang , Beth Ann Heckel
Abstract: An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving an image of a region of interest of a patient with an enteric tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by superimposing graphical markers on the image that indicate placement or misplacement of the enteric tube or line, and displaying the combined image on a display. In further aspects, a classification of the enteric tube or line (e.g., correctly placed tube present, malpositioned tube present, and so forth) may be determined and communicated to one or more clinicians. Additionally, the outputs of the image processing system may also be provided to facilitate triage of patients, helping prioritize which tube placements require further attention and in what order.
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公开(公告)号：US20250029370A1
公开(公告)日：2025-01-23
申请号：US18356461
申请日：2023-07-21
Applicant: GE Precision Healthcare LLC
Inventor： Xiaomeng Dong , Michael Potter , Hongxu Yang , Junpyo Hong , Ravi Soni , Gopal Biligeri Avinash
IPC: G06V10/774 , G06V10/776 , G06V10/82
Abstract: In various embodiments, a system can: access a failure image on which a first model has inaccurately performed an inferencing task; train, on a set of dummy images, a second model to learn a visual variety of the failure image, based on a loss function having a first term and a second term, the first term quantifying visual content dissimilarities between the set of dummy images and outputs predicted during training by the second model, and the second term quantifying, at a plurality of different image scales, visual variety dissimilarities between the failure image and the outputs predicted during training by the second model; and execute the second model on each of a set of training images on which the first model was trained, thereby yielding a set of first converted training images that exhibit the visual variety of the failure image.
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公开(公告)号：US20230177706A1
公开(公告)日：2023-06-08
申请号：US17543283
申请日：2021-12-06
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Balázs Péter Cziria , Pál Tegzes , Gopal Biligeri Avinash , German Guillermo Vera Gonzalez , Lehel Mihály Ferenczi , Zita Herczeg , Ravi Soni , Dibyajyoti Pati
CPC classification number: G06T7/30 , G06K9/6256 , G06T2207/20081 , G06T2207/20084
Abstract: Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.
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公开(公告)号：US20240420349A1
公开(公告)日：2024-12-19
申请号：US18814832
申请日：2024-08-26
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Balázs Péter Cziria , Pál Tegzes , Gopal Biligeri Avinash , German Guillermo Vera Gonzalez , Lehel Mihály Ferenczi , Zita Herczeg , Ravi Soni , Dibyajyoti Pati
IPC: G06T7/30 , G06F18/214
Abstract: Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.
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公开(公告)号：US20230162355A1
公开(公告)日：2023-05-25
申请号：US17993612
申请日：2022-11-23
Applicant: GE Precision Healthcare LLC
Inventor： Pal Tegzes , Zita Herczeg , Hongxu Yang , Zoltan Kiss , Balazs Peter Cziria , Poonam Dalal , Alec Joseph Baenen , Gireesha Chinthamani Rao , Beth Ann Heckel , Pulak Goswami , Dennis Wei Zhou , Gopal Biligeri Avinash , Lehel Ferenczi , Katelyn Rose Nye
CPC classification number: G06T7/0012 , G06T7/10 , G06T2207/20081
Abstract: An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving images of a region of interest of a patient with an enteric tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by combining the received images, and superimposing graphical markers on the combined image that indicate placement or misplacement of the enteric tube or line, and displaying the combined image on a display. In further aspects, a classification of the enteric tube or line (e.g., correctly placed tube present, malpositioned tube present, and so forth) and a detected positional change in the placement of the enteric tube or line may be determined and communicated to one or more clinicians. Additionally, the outputs of the image processing system may also be provided to facilitate triage of patients, helping prioritize which tube placements require further attention and in what order.
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Patent Agency Ranking
公开(公告)号：US20210232909A1
公开(公告)日：2021-07-29
申请号：US16773156
申请日：2020-01-27
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Min Zhang , Gopal Biligeri Avinash , Lehel Ferenczi , Levente Imre Török , Pál Tegzes
Abstract: Systems and techniques that facilitate freeze-out as a regularizer in training neural networks are presented. A system can include a memory and a processor that executes computer executable components. The computer executable components can include: an assessment component that identifies units of a neural network, a selection component that selects a subset of units of the neural network, and a freeze-out component that freezes the selected subset of units of the neural network so that weights of output connections from the frozen subset of units will not be updated for a training run.
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公开(公告)号：US20240346291A1
公开(公告)日：2024-10-17
申请号：US18300807
申请日：2023-04-14
Applicant: GE Precision Healthcare LLC
Inventor： Xiaomeng Dong , Michael Potter , Hongxu Yang , Ravi Soni , Gopal Biligeri Avinash
IPC: G06N3/0455 , G06N3/082
CPC classification number: G06N3/0455 , G06N3/082
Abstract: Techniques are described for multi-task neural network model design using task crystallization are described. In one example a task crystallization method comprises adding one or more task-specific channels to a backbone neural network adapted to perform a primary inferencing task to generate a multi-task neural network model, wherein the adding comprises adding task-specific elements to different layers of the backbone neural network for each channel of the one or more task-specific channels. The method further comprises training, by the system, the one or more task-specific channels to perform one or more additional inferencing tasks that are respectively different from one another and the primary inferencing task, comprising separately tuning and crystallizing the task-specific elements of each channel of the one or more task-specific channels.
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公开(公告)号：US12100170B2
公开(公告)日：2024-09-24
申请号：US17543283
申请日：2021-12-06
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Balázs Péter Cziria , Pál Tegzes , Gopal Biligeri Avinash , German Guillermo Vera Gonzalez , Lehel Mihály Ferenczi , Zita Herczeg , Ravi Soni , Dibyajyoti Pati
IPC: G06T7/30 , G06F18/214
CPC classification number: G06T7/30 , G06F18/214 , G06T2207/20081 , G06T2207/20084
Abstract: Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.
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公开(公告)号：US20240193761A1
公开(公告)日：2024-06-13
申请号：US18064541
申请日：2022-12-12
Applicant: GE Precision Healthcare LLC
Inventor： Hongxu Yang , Gopal Biligeri Avinash , Lehel Mihály Ferenczi , Xiaomeng Dong , Najib Akram Maheen Aboobacker , Gireesha Chinthamani Rao , Tao Tan , German Guillermo Vera Gonzalez
CPC classification number: G06T7/0012 , G06T3/4046 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004
Abstract: Systems/techniques that facilitate improved deep learning image processing are provided. In various embodiments, a system can access a medical image, wherein pixels or voxels of the medical image can be allocated among a plurality of regions. In various aspects, the system can generate, via execution of a deep learning neural network on the medical image, a set of region-wise parameter maps, wherein a region-wise parameter map can consist of one predicted parameter per region of the medical image. In various instances, the system can generate a transformed version of the medical image by feeding the set of region-wise parameter maps to an analytical transformation function. In various cases, the system can render the transformed version of the medical image on an electronic display. In various aspects, the plurality of regions can be irregular or tissue-based.
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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
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.
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