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公开(公告)号：US11842485B2
公开(公告)日：2023-12-12
申请号：US17192804
申请日：2021-03-04
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Máté Fejes , Gopal Avinash , Ravi Soni , Bipul Das , Rakesh Mullick , Pál Tegzes , Lehel Ferenczi , Vikram Melapudi , Krishna Seetharam Shriram
CPC classification number: G06T7/0012 , G06N3/08 , G06T15/08 , G06T2207/10088 , G06T2207/10104
Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.
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公开(公告)号：US20230342427A1
公开(公告)日：2023-10-26
申请号：US18343266
申请日：2023-06-28
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Gopal B. Avinash , Máté Fejes , Ravi Soni , Dániel Attila Szabó , Rakesh Mullick , Vikram Melapudi , Krishna Seetharam Shriram , Sohan Rashmi Ranjan , Bipul Das , Utkarsh Agrawal , László Ruskó , Zita Herczeg , Barbara Darázs
IPC: G06F18/214 , G06N5/04 , G16H30/40 , A61B5/055 , G06T5/50 , G06F18/21 , G06T7/30 , A61B5/00 , G16H30/20 , G16H50/20 , G16H50/50 , A61B6/03 , G06F18/22 , G06F18/28 , A61B6/00
CPC classification number: G06F18/214 , A61B5/055 , A61B5/7267 , A61B6/032 , A61B6/5223 , G06F18/2178 , G06F18/22 , G06F18/28 , G06N5/04 , G06T5/50 , G06T7/30 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/50 , G06T2200/04 , G06T2207/10081 , G06T2207/10116 , G06T2207/20081 , G06T2207/20084 , G06T2207/20212 , G06T2207/30004 , G06V2201/03
Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.
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公开(公告)号：US20230306601A1
公开(公告)日：2023-09-28
申请号：US17656171
申请日：2022-03-23
Applicant: GE Precision Healthcare LLC
Inventor： László Ruskó , Vanda Czipczer , Bernadett Kolozsvári , Richárd Zsámboki , Tao Tan , Balázs Péter Cziria , Attila Márk Rádics , Lehel Ferenczi , Fei Mian , Hongxiang YI , Florian Wiesinger
CPC classification number: G06T7/11 , G06T7/0012 , G06T7/12 , G06T11/203 , G06T11/001 , G16H30/40 , G16H30/20 , G06T2207/30168
Abstract: Methods and systems are provided for segmenting structures in medical images. In one embodiment, a method includes receiving an input dataset including a set of medical images, a structure list specifying a set of structures to be segmented, and a segmentation protocol, performing an input check on the input dataset, determining whether each medical image of the set of medical images has passed the input check and removing any medical images from the set of medical images that do not pass the input check to form a final set of medical images, segmenting each structure from the structure list using one or more segmentation models and the final set of medical images, receiving a set of segmentations output from the one or more segmentation models, processing the set of segmentations to generate a final set of segmentations, and displaying and/or saving in memory the final set of segmentations.
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24.发明公开公开(公告)号：US20230284986A1
公开(公告)日：2023-09-14
申请号：US17690258
申请日：2022-03-09
Applicant: GE Precision Healthcare LLC
Inventor： Dejun Wang , Buer Qi , Tao Tan , Gireesha Chinthamani Rao , Gopal B. Avinash , Qingming Peng , Yaan Ge , Sylvain Bernard , Vincent Bismuth
CPC classification number: A61B6/4429 , A61B6/5205 , A61B6/025 , G06T3/4046 , G06V10/25 , G06N3/0454
Abstract: A tomosynthesis machine allows for faster image acquisition and improved signal-to-noise by acquiring a projection attenuation data and using machine learning to identify a subset of the projection attenuation data for the production of thinner slices and/or higher resolution slices using machine learning.
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公开(公告)号：US11669945B2
公开(公告)日：2023-06-06
申请号：US16858862
申请日：2020-04-27
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Pál Tegzes , Levente Imre Török , Lehel Ferenczi , Gopal B. Avinash , László Ruskó , Gireesha Chinthamani Rao , Khaled Younis , Soumya Ghose
IPC: G06F18/21 , G06V10/772 , G06V10/774 , G06V10/762 , G06V10/74 , G06V10/776 , G06T7/00 , G06V10/82 , G06F18/22 , G06F18/23 , G06F18/28 , G06F18/214
CPC classification number: G06F18/217 , G06F18/214 , G06F18/22 , G06F18/23 , G06F18/28 , G06T7/00 , G06V10/761 , G06V10/762 , G06V10/772 , G06V10/774 , G06V10/776 , G06V10/82
Abstract: Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.
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Patent Agency Ranking
公开(公告)号：US20230162352A1
公开(公告)日：2023-05-25
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 Chintharmani Rao , Lehel Ferenczi , Gopal Biligeri Avinash , Zoltan Kiss , Hongxu Yang , Beth Ann Heckel
CPC classification number: G06T7/0012 , G16H50/20 , 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 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：US11537885B2
公开(公告)日：2022-12-27
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：US20220101048A1
公开(公告)日：2022-03-31
申请号：US17093960
申请日：2020-11-10
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Gopal B. Avinash , Máté Fejes , Ravi Soni , Dániel Attila Szabó , Rakesh Mullick , Vikram Melapudi , Krishna Seetharam Shriram , Sohan Rashmi Ranjan , Bipul Das , Utkarsh Agrawal , László Ruskó , Zita Herczeg , Barbara Darázs
IPC: G06K9/62 , G06T5/50 , G06T7/30 , G06N5/04 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/50 , A61B6/03 , A61B6/00 , A61B5/055 , A61B5/00
Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.
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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：US12159420B2
公开(公告)日：2024-12-03
申请号：US17457179
申请日：2021-12-01
Applicant: GE Precision Healthcare LLC
Inventor： Dibyajyoti Pati , Junpyo Hong , Venkata Ratnam Saripalli , German Guillermo Vera Gonzalez , Dejun Wang , Aizhen Zhou , Gopal B. Avinash , Ravi Soni , Tao Tan , Fuqiang Chen , Yaan Ge
Abstract: Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.
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