Patents by Inventor Soumya Ghose
Soumya Ghose has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11948677Abstract: Systems and techniques that facilitate hybrid unsupervised and supervised image segmentation are provided. In various embodiments, a system can access a computed tomography (CT) image depicting an anatomical structure. In various aspects, the system can generate, via an unsupervised modeling technique, at least one class probability mask of the anatomical structure based on the CT image. In various instances, the system can generate, via a deep-learning model, an image segmentation based on the CT image and based on the at least one class probability mask.Type: GrantFiled: June 8, 2021Date of Patent: April 2, 2024Assignee: GE PRECISION HEALTHCARE LLCInventors: Soumya Ghose, Jhimli Mitra, Peter M Edic, Prem Venugopal, Jed Douglas Pack
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Publication number: 20240029415Abstract: Systems and methods are provided for an image processing system. In an example, a method includes acquiring a pathology dataset, acquiring a reference dataset, generating a deformation field by mapping points of a reference case of the reference dataset to points of a patient image of the pathology dataset, manipulating the deformation field, applying the deformation field to the reference case to generate a simulated pathology image including a simulated deformation pathology, and outputting the simulated pathology image.Type: ApplicationFiled: July 25, 2022Publication date: January 25, 2024Inventors: Dattesh Dayanand Shanbhag, Chitresh Bhushan, Soumya Ghose, Deepa Anand
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Publication number: 20240005480Abstract: Methods and systems are provided for automatic placement of at least one saturation band on a medical image, which may direct saturation pulses during a MRI scan. A method may include acquiring a localizer image of an imaging subject, determining a plane mask for the localizer image by entering the localizer image as input to a deep neural network trained to output the plane mask based on the localizer image, generating a saturation band based on the plane mask by positioning the saturation band at a position and an angulation of the plane mask, and outputting a graphical prescription for display on a display device, the graphical prescription including the saturation band overlaid on the medical image.Type: ApplicationFiled: July 1, 2022Publication date: January 4, 2024Inventors: Chitresh Bhushan, Dattesh Dayanand Shanbhag, Soumya Ghose, Amod Suhas Jog
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Publication number: 20230317293Abstract: A method for determining a recurrence of a disease in a patient is presented. The method includes generating a plurality of medical images of an organ of the patient and determining a plurality of recurrence probabilities from the plurality of medical images. A recurrence of the disease is determined based on the plurality of recurrence probabilities and clinicopathological data of the patient using a Bayesian network.Type: ApplicationFiled: March 31, 2022Publication date: October 5, 2023Inventors: Sanghee Cho, Zhanpan Zhang, Soumya Ghose, Fiona Ginty, Cynthia Elizabeth Landberg Davis, Jhimli Mitra, Sunil S. Badve, Yesim Gokmen-Polar
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Publication number: 20230307137Abstract: A method for determining a recurrence of a disease in a patient includes generating a medical image of an organ of the patient and then extracting an invasive edge around an area of interest in the medical image. A plurality of radiomics features is obtained from the invasive edge and the recurrence of the disease is determined based on the plurality of radiomics features.Type: ApplicationFiled: March 25, 2022Publication date: September 28, 2023Inventors: Soumya Ghose, Fiona Ginty, Cynthia Elizabeth Landberg Davis, Sanghee Cho, Sunil S. Badve, Yesim Gokmen-Polar
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Publication number: 20230252614Abstract: 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.Type: ApplicationFiled: April 21, 2023Publication date: August 10, 2023Inventors: Tao Tan, Pál Tegzes, Levente Imre Török, Lehel Ferenczi, Gopal B. Avinash, László Ruskó, Gireesha Chinthamani Rao, Khaled Younis, Soumya Ghose
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Patent number: 11669945Abstract: 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.Type: GrantFiled: April 27, 2020Date of Patent: June 6, 2023Assignee: GE PRECISION HEALTHCARE LLCInventors: Tao Tan, Pál Tegzes, Levente Imre Török, Lehel Ferenczi, Gopal B. Avinash, László Ruskó, Gireesha Chinthamani Rao, Khaled Younis, Soumya Ghose
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Publication number: 20230142152Abstract: A computer-implemented method includes generating, via a processor, synthetic vessels. The method also includes performing, via the processor, three-dimensional (3D) computational fluid dynamics (CFD) on the synthetic vessels for different flow rates to generate 3D CFD data. The method further includes extracting, via the processor, 3D image patches from the synthetic vessels. The method even further includes obtaining, via the processor, pressure drops across the 3D image patches from the 3D CFD data. The method yet further includes training, via the processor, a deep neural network utilizing the 3D image patches, the pressure drops, and associated flow rates to generate a trained deep neural network.Type: ApplicationFiled: November 5, 2021Publication date: May 11, 2023Inventors: Prem Venugopal, Cynthia Elizabeth Landberg Davis, Jed Douglas Pack, Jhimli Mitra, Soumya Ghose, Peter Michael Edic
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Publication number: 20230144624Abstract: A computer-implemented method includes obtaining, via a processor, segmented image patches of a vessel along a coronary tree path and associated coronary flow distribution for respective vessel segments in the segmented image patches. The method also includes determining, via the processor, a pressure drop distribution along an axial length of the vessel from the segmented image patches and the associated coronary flow distribution. The method further includes determining, via the processor, critical points in the pressure drop distribution. The method even further includes detecting, via the processor, a presence of a stenosis based on the critical points in the pressure drop distribution.Type: ApplicationFiled: November 5, 2021Publication date: May 11, 2023Inventors: Prem Venugopal, Cynthia Elizabeth Landberg Davis, Jed Douglas Pack, Jhimli Mitra, Soumya Ghose
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Publication number: 20230094940Abstract: A deep learning-based continuous federated learning network system is provided. The system includes a global site comprising a global model and a plurality of local sites having a respective local model derived from the global model. The plurality of model tuning modules having a processing system are provided at the plurality of local sites for tuning the respective local model. The processing system is programmed to receive incremental data and select one or more layers of the local model for tuning based on the incremental data. Finally, the selected layers are tuned to generate a retrained model.Type: ApplicationFiled: September 27, 2021Publication date: March 30, 2023Inventors: Radhika Madhavan, Soumya Ghose, Dattesh Dayanand Shanbhag, Andre De Almeida Maximo, Chitresh Bhushan, Desmond Teck Beng Yeo, Thomas Kwok-Fah Foo
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Publication number: 20230004872Abstract: A computer implemented method is provided. The method includes establishing, via multiple processors, a continuous federated learning framework including a global model at a global site and respective local models derived from the global model at respective local sites. The method also includes retraining or retuning, via the multiple processors, the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets generated from the actual datasets.Type: ApplicationFiled: July 1, 2021Publication date: January 5, 2023Inventors: Soumya Ghose, Radhika Madhavan, Chitresh Bhushan, Dattesh Dayanand Shanbhag, Deepa Anand, Desmond Teck Beng Yeo, Thomas Kwok-Fah Foo
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Publication number: 20220392616Abstract: Systems and techniques that facilitate hybrid unsupervised and supervised image segmentation are provided. In various embodiments, a system can access a computed tomography (CT) image depicting an anatomical structure. In various aspects, the system can generate, via an unsupervised modeling technique, at least one class probability mask of the anatomical structure based on the CT image. In various instances, the system can generate, via a deep-learning model, an image segmentation based on the CT image and based on the at least one class probability mask.Type: ApplicationFiled: June 8, 2021Publication date: December 8, 2022Inventors: Soumya Ghose, Jhimli Mitra, Peter M Edic, Prem Venugopal, Jed Douglas Pack
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Publication number: 20220335597Abstract: Systems and methods for workflow management for labeling the subject anatomy are provided. The method comprises obtaining at least one localizer image of a subject anatomy using a low-resolution medical imaging device. The method further comprises labeling at least one anatomical point within the at least one localizer image. The method further comprises extracting using a machine learning module a mask of the at least one localizer image comprising the at least one anatomical point label. The method further comprises using the mask to label at least one anatomical point on a high-resolution image of the subject anatomy based on the at least one anatomical point within the localizer image.Type: ApplicationFiled: April 19, 2021Publication date: October 20, 2022Inventors: Dattesh Shanbhag, Deepa Anand, Chitresh Bhushan, Arathi Sreekumari, Soumya Ghose
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Patent number: 11304683Abstract: The subject matter discussed herein relates to multi-modal image alignment to facilitate biopsy procedures and post-biopsy procedures. In one such example, prostate structures (or other suitable anatomic features or structures) are automatically segmented in pre-biopsy MR and pre-biopsy ultrasound images. Thereafter, pre-biopsy MR and pre-biopsy ultrasound contours are aligned. To account for non-linear deformation of the imaged anatomic structure, a patient-specific transformation model is trained via deep learning based at least in part on the pre-biopsy ultrasound images. The pre-biopsy ultrasound images that are overlaid with the pre-biopsy MR contours and based off the deformable transformation model are then aligned with the biopsy ultrasound images. Such real-time alignment using multi-modality imaging techniques provides guidance during the biopsy and post-biopsy system.Type: GrantFiled: September 13, 2019Date of Patent: April 19, 2022Assignee: General Electric CompanyInventors: Jhimli Mitra, Thomas Kwok-Fah Foo, Desmond Teck Beng Yeo, David Martin Mills, Soumya Ghose, Michael John MacDonald
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Publication number: 20220114389Abstract: A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates.Type: ApplicationFiled: October 9, 2020Publication date: April 14, 2022Inventors: Soumya Ghose, Dattesh Dayanand Shanbhag, Chitresh Bhushan, Andre De Almeida Maximo, Radhika Madhavan, Desmond Teck Beng Yeo, Thomas Kwok-Fah Foo
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Patent number: 11284851Abstract: Embodiments access a set of radiological images acquired from a population of subjects, where a member of the set of radiological images includes a left atrium (LA) region; construct a statistical shape differential atlas from the images; generate a template LA model from the statistical shape differential atlas, where the template LA model includes a site of interest (SOI); acquire a pre-ablation radiological image of a region of tissue in a patient demonstrating atrial fibrillation (AF) pathology; generate a patient LA model from the pre-ablation image; compute a deformation field that registers the SOI to the patient LA model using deformable registration; compute a patient feature vector based on the deformation field; generate an AF probability score for the patient based on the feature vector; generate a classification of the patient based, at least in part, on the AF probability score; and display the classification or the AF probability score.Type: GrantFiled: March 22, 2019Date of Patent: March 29, 2022Assignee: Case Western Reserve UniversityInventors: Anant Madabhushi, Thomas Atta-Fosu, Soumya Ghose
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Publication number: 20210334598Abstract: 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.Type: ApplicationFiled: April 27, 2020Publication date: October 28, 2021Inventors: Tao Tan, Pál Tegzes, Levente Imre Török, Lehel Ferenczi, Gopal B. Avinash, László Ruskó, Gireesha Chinthamani Rao, Khaled Younis, Soumya Ghose
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Patent number: 10957010Abstract: The subject matter discussed herein relates to the automatic, real-time registration of pre-operative magnetic resonance imaging (MRI) data to intra-operative ultrasound (US) data (e.g., reconstructed images or unreconstructed data), such as to facilitate surgical guidance or other interventional procedures. In one such example, brain structures (or other suitable anatomic features or structures) are automatically segmented in pre-operative and intra-operative ultrasound data. Thereafter, anatomic structure (e.g., brain structure) guided registration is applied between pre-operative and intra-operative ultrasound data to account for non-linear deformation of the imaged anatomic structure. MR images that are pre-registered to pre-operative ultrasound images are then given the same nonlinear spatial transformation to align the MR images with intra-operative ultrasound images to provide surgical guidance.Type: GrantFiled: August 7, 2019Date of Patent: March 23, 2021Assignee: General Electric CompanyInventors: Soumya Ghose, Jhimli Mitra, David Martin Mills, Lowell Scott Smith, Desmond Teck Beng Yeo, Thomas Kwok-Fah Foo
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Publication number: 20210077077Abstract: The subject matter discussed herein relates to multi-modal image alignment to facilitate biopsy procedures and post-biopsy procedures. In one such example, prostate structures (or other suitable anatomic features or structures) are automatically segmented in pre-biopsy MR and pre-biopsy ultrasound images. Thereafter, pre-biopsy MR and pre-biopsy ultrasound contours are aligned. To account for non-linear deformation of the imaged anatomic structure, a patient-specific transformation model is trained via deep learning based at least in part on the pre-biopsy ultrasound images. The pre-biopsy ultrasound images that are overlaid with the pre-biopsy MR contours and based off the deformable transformation model are then aligned with the biopsy ultrasound images. Such real-time alignment using multi-modality imaging techniques provides guidance during the biopsy and post-biopsy system.Type: ApplicationFiled: September 13, 2019Publication date: March 18, 2021Inventors: Jhimli Mitra, Thomas Kwok-Fah Foo, Desmond Teck Beng Yeo, David Martin Mills, Soumya Ghose, Michael John MacDonald
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Publication number: 20210042878Abstract: The subject matter discussed herein relates to the automatic, real-time registration of pre-operative magnetic resonance imaging (MRI) data to intra-operative ultrasound (US) data (e.g., reconstructed images or unreconstructed data), such as to facilitate surgical guidance or other interventional procedures. In one such example, brain structures (or other suitable anatomic features or structures) are automatically segmented in pre-operative and intra-operative ultrasound data. Thereafter, anatomic structure (e.g., brain structure) guided registration is applied between pre-operative and intra-operative ultrasound data to account for non-linear deformation of the imaged anatomic structure. MR images that are pre-registered to pre-operative ultrasound images are then given the same nonlinear spatial transformation to align the MR images with intra-operative ultrasound images to provide surgical guidance.Type: ApplicationFiled: August 7, 2019Publication date: February 11, 2021Inventors: Soumya Ghose, Jhimli Mitra, David Martin Mills, Lowell Scott Smith, Desmond Teck Beng Yeo, Thomas Kwok-Fah Foo