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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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公开(公告)号：US20230084202A1
公开(公告)日：2023-03-16
申请号：US17474711
申请日：2021-09-14
Applicant: GE Precision Healthcare LLC
Inventor： Abhijit Patil , Rakesh Mullick , Bipul Das
IPC: G06F21/62
Abstract: Techniques are described that that facilitate securely deploying artificial intelligence (AI) models and distributing inferences generated therefrom. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise an algorithm execution component that applies an AI model to input data and generates output data, and an encryption component that encrypts the output data using a proprietary encryption mechanism, resulting in encrypted output data. The proprietary encryption mechanism can include a mechanism that prevents usage and rendering of the encrypted output data without decryption of the encrypted output data using a proprietary decryption mechanism.
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公开(公告)号：US20220092768A1
公开(公告)日：2022-03-24
申请号：US17122709
申请日：2020-12-15
Applicant: GE Precision Healthcare LLC
Inventor： Vikram Melapudi , Bipul Das , Krishna Seetharam Shriram , Prasad Sudhakar , Rakesh Mullick , Sohan Rashmi Ranjan , Utkarsh Agarwal
Abstract: Techniques are provided for generating enhanced image representations from original X-ray images using deep learning techniques. In one embodiment, a system is provided that includes a memory storing computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a reception component, an analysis component, and an artificial intelligence component. The analysis component analyzes the original X-ray image using an AI-based model with respect to a set of features of interest. The AI component generates a plurality of enhanced image representations. Each enhanced image representation highlights a subset of the features of interest and suppresses remaining features of interest in the set that are external to the subset.
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公开(公告)号：US12014823B2
公开(公告)日：2024-06-18
申请号：US16557797
申请日：2019-08-30
Applicant: GE Precision Healthcare LLC
Abstract: Various methods and systems are provided for computer-aided diagnosis. In one embodiment, a method comprises acquiring, with an imaging system, a medical image of a subject, generating, with a radiologist model associated with a radiologist of an institution, a computer-aided diagnosis for the medical image, the radiologist model comprising a deep neural network trained on a plurality of diagnoses provided by the radiologist, displaying, to the radiologist via a display device, the medical image and the computer-aided diagnosis, and selectively updating, based on the medical image, one or more of the radiologist model, an institution model associated with the institution, and a geographic model associated with a geographic area containing the institution. In this way, a radiologist may be assisted by a deep neural network model configured as a digital twin of the radiologist.
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公开(公告)号：US11657501B2
公开(公告)日：2023-05-23
申请号：US17122709
申请日：2020-12-15
Applicant: GE Precision Healthcare LLC
Inventor： Vikram Melapudi , Bipul Das , Krishna Seetharam Shriram , Prasad Sudhakar , Rakesh Mullick , Sohan Rashmi Ranjan , Utkarsh Agarwal
CPC classification number: G06T7/0012 , A61B6/482 , G06T5/00 , G06T7/10 , G06T11/003 , G06T2207/10081 , G06T2207/10088 , G06T2207/10104 , G06T2207/10116 , G06T2207/20081
Abstract: Techniques are provided for generating enhanced image representations from original X-ray images using deep learning techniques. In one embodiment, a system is provided that includes a memory storing computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a reception component, an analysis component, and an artificial intelligence component. The analysis component analyzes the original X-ray image using an AI-based model with respect to a set of features of interest. The AI component generates a plurality of enhanced image representations. Each enhanced image representation highlights a subset of the features of interest and suppresses remaining features of interest in the set that are external to the subset.
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Patent Agency Ranking
公开(公告)号：US20220358692A1
公开(公告)日：2022-11-10
申请号：US17307517
申请日：2021-05-04
Applicant: GE Precision Healthcare LLC
Inventor： Chitresh Bhushan , Dattesh Dayanand Shanbhag , Rakesh Mullick
Abstract: Techniques are described for generating reformatted views of a three-dimensional (3D) anatomy scan using deep-learning estimated scan prescription masks. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a mask generation component that employs a pre-trained neural network model to generate masks for different anatomical landmarks depicted in one or more calibration images captured of an anatomical region of a patient. The computer executable components further comprise a reformatting component that reformats 3D image data captured of the anatomical region of the patient using the masks to generate different representations of the 3D image data that correspond to the different anatomical landmarks.
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公开(公告)号：US20210077059A1
公开(公告)日：2021-03-18
申请号：US16575092
申请日：2019-09-18
Applicant: GE Precision Healthcare LLC
Inventor： Krishna Seetharam Shriram , Arathi Sreekumari , Rakesh Mullick
Abstract: Systems and methods are provided for projection profile enabled computer aided detection (CAD). Volumetric ultrasound dataset may be generated, based on echo ultrasound signals, and based on the volumetric ultrasound dataset, a three-dimensional (3D) ultrasound volume may generated. Selective structure detection may be applied to the three-dimensional (3D) ultrasound volume. The selective structure detection may include generating based on a projection of the three-dimensional (3D) ultrasound volume in a particular spatial direction, a two-dimensional (2D) image; applying two-dimensional (2D) structure detection to the two-dimensional (2D) image, to identify structure candidates associated with a particular type of structures; selecting for each identified structure candidate, a corresponding local volume within the three-dimensional (3D) ultrasound volume; applying three-dimensional (3D) structure detection to each selected local volume; and identifying based on applying the three-dimensional (3D) structure detection, one or more structure candidates that match the particular type of structures.
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公开(公告)号：US20240203039A1
公开(公告)日：2024-06-20
申请号：US18065964
申请日：2022-12-14
Applicant: GE Precision Healthcare LLC
Inventor： Deepa Anand , Bipul Das , Vanika Singhal , Rakesh Mullick , Sanjay Kumar NT
Abstract: Systems/techniques that facilitate interpretable task-specific dimensionality-reduction are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can generate, via execution of a first deep learning neural network, a voxel-wise weight map corresponding to the three-dimensional medical image and a set of projection vectors corresponding to the three-dimensional medical image. In various instances, the system can generate a set of two-dimensional projection images of the three-dimensional medical image, based on the voxel-wise weight map and the set of projection vectors. In various cases, the first deep learning neural network can be trained in a serial pipeline with a second deep learning neural network that is configured to perform an inferencing task on two-dimensional inputs. This can cause the set of two-dimensional projection images to be tailored to the inferencing task.
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19.发明公开公开(公告)号：US20240078669A1
公开(公告)日：2024-03-07
申请号：US18497912
申请日：2023-10-30
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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20.发明公开公开(公告)号：US20230342913A1
公开(公告)日：2023-10-26
申请号：US17660717
申请日：2022-04-26
Applicant: GE Precision Healthcare LLC
Inventor： Mahendra Madhukar Patil , Rakesh Mullick , Sudhanya Chatterjee , Syed Asad Hashmi , Dattesh Dayanand Shanbhag , Deepa Anand , Suresh Emmanuel Devadoss Joel
CPC classification number: G06T7/0012 , G06N20/00 , G06V10/25 , G06V2201/03
Abstract: Techniques are described for generating high quality training data collections for training artificial intelligence (AI) models in the medical imaging domain. A method embodiment comprises receiving, by a system comprising processor, input indicating a clinical context associated with usage of a medical image dataset, and selecting, by the system, one or more data scrutiny metrics for filtering the medical image dataset based on the clinical context. The method further comprises applying, by the system, one or more image processing functions to the medical image dataset to generate metric values of the one or more data scrutiny metrics for respective medical images included in the medical image dataset, filtering, by the system, the medical image dataset into one or more subsets based on one or more acceptability criteria for the metric values.
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