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公开(公告)号：US12125217B2
公开(公告)日：2024-10-22
申请号：US17520254
申请日：2021-11-05
发明人： Prem Venugopal , Cynthia Elizabeth Landberg Davis , Jed Douglas Pack , Jhimli Mitra , Soumya Ghose
CPC分类号： G06T7/248 , G06T7/10 , G16H30/20 , G06T2207/30104
摘要： 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.
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公开(公告)号：US20240029415A1
公开(公告)日：2024-01-25
申请号：US17814746
申请日：2022-07-25
CPC分类号： G06V10/7747 , G06V10/7715 , G06T19/20 , G06T15/08 , G06T7/0014 , G16H30/40 , G16H50/50 , G06T2207/20081 , G06T2207/30096 , G06T2207/30012 , G06V2201/033 , G06T2219/2021 , G06T2210/41
摘要： 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.
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公开(公告)号：US20240260919A1
公开(公告)日：2024-08-08
申请号：US18636614
申请日：2024-04-16
发明人： Prem Venugopal , Cynthia Elizabeth Landberg Davis , Jed Douglas Pack , Jhimli Mitra , Soumya Ghose , Peter Michael Edic
CPC分类号： A61B6/504 , A61B6/032 , A61B6/037 , A61B6/507 , A61B6/5217 , A61B6/5241 , G06N3/08 , G06T7/11
摘要： A computer-implemented method includes obtaining, via a processor, clinical images including vessels and generating, via the processor, straightened-out images for each coronary tree path within respective clinical images, The method also includes extracting, via the processor, segmented 3D image patches, determining, via the processor, overlapping binary mask volumes for each segment, and predicting, via the processor, pressure drops across the segmented image patches using a trained deep neural network.
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公开(公告)号：US12039007B2
公开(公告)日：2024-07-16
申请号：US17067179
申请日：2020-10-09
发明人： Soumya Ghose , Dattesh Dayanand Shanbhag , Chitresh Bhushan , Andre De Almeida Maximo , Radhika Madhavan , Desmond Teck Beng Yeo , Thomas Kwok-Fah Foo
IPC分类号： G06F18/214 , G06F18/211 , G06F18/22 , G06F18/232 , G06N3/08 , G16H30/40
CPC分类号： G06F18/2148 , G06F18/211 , G06F18/2155 , G06F18/22 , G06F18/232 , G06N3/08 , G16H30/40 , G06V2201/03
摘要： 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. In addition, the method includes updating the training dataset using the test image and the optimal label, retraining the neural network model, generating a label of an unlabeled image using the retrained neural network model, and outputting the generated label.
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公开(公告)号：US11948677B2
公开(公告)日：2024-04-02
申请号：US17342280
申请日：2021-06-08
发明人： Soumya Ghose , Jhimli Mitra , Peter M Edic , Prem Venugopal , Jed Douglas Pack
CPC分类号： G16H30/40 , G06N3/045 , G06N3/088 , G06T7/10 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/30101
摘要： 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.
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公开(公告)号：US20230004872A1
公开(公告)日：2023-01-05
申请号：US17365650
申请日：2021-07-01
发明人： Soumya Ghose , Radhika Madhavan , Chitresh Bhushan , Dattesh Dayanand Shanbhag , Deepa Anand , Desmond Teck Beng Yeo , Thomas Kwok-Fah Foo
摘要： 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.
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公开(公告)号：US11980492B2
公开(公告)日：2024-05-14
申请号：US17520204
申请日：2021-11-05
发明人： Prem Venugopal , Cynthia Elizabeth Landberg Davis , Jed Douglas Pack , Jhimli Mitra , Soumya Ghose , Peter Michael Edic
CPC分类号： A61B6/504 , A61B6/032 , A61B6/037 , A61B6/507 , A61B6/5217 , A61B6/5241 , G06N3/08 , G06T7/11
摘要： 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.
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公开(公告)号：US20230317293A1
公开(公告)日：2023-10-05
申请号：US17710326
申请日：2022-03-31
发明人： Sanghee Cho , Zhanpan Zhang , Soumya Ghose , Fiona Ginty , Cynthia Elizabeth Landberg Davis , Jhimli Mitra , Sunil S. Badve , Yesim Gokmen-Polar
CPC分类号： G16H50/30 , G16H30/00 , G06T7/0014 , G06N3/0454 , G06T2207/30068
摘要： 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.
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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
发明人： Tao Tan , Pál Tegzes , Levente Imre Török , Lehel Ferenczi , Gopal B. Avinash , László Ruskó , Gireesha Chinthamani Rao , Khaled Younis , Soumya Ghose
IPC分类号： 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分类号： G06F18/217 , G06F18/214 , G06F18/22 , G06F18/23 , G06F18/28 , G06T7/00 , G06V10/761 , G06V10/762 , G06V10/772 , G06V10/774 , G06V10/776 , G06V10/82
摘要： 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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公开(公告)号：US20230094940A1
公开(公告)日：2023-03-30
申请号：US17486796
申请日：2021-09-27
发明人： Radhika Madhavan , Soumya Ghose , Dattesh Dayanand Shanbhag , Andre De Almeida Maximo , Chitresh Bhushan , Desmond Teck Beng Yeo , Thomas Kwok-Fah Foo
摘要： 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.
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