Patents by Inventor Bogdan Georgescu

Bogdan Georgescu 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).

  • Publication number: 20200193594
    Abstract: Systems and methods for identifying and assessing lymph nodes are provided. Medical image data (e.g., one or more computed tomography images) of a patient is received and anatomical landmarks in the medical image data are detected. Anatomical objects are segmented from the medical image data based on the one or more detected anatomical landmarks. Lymph nodes are identified in the medical image data based on the one or more detected anatomical landmarks and the one or more segmented anatomical objects. The identified lymph nodes may be assessed by segmenting the identified lymph nodes from the medical image data and quantifying the segmented lymph nodes. The identified lymph nodes and/or the assessment of the identified lymph nodes are output.
    Type: Application
    Filed: December 13, 2019
    Publication date: June 18, 2020
    Inventors: Bogdan Georgescu, Elijah D. Bolluyt, Alexandra Comaniciu, Sasa Grbic
  • Patent number: 10671833
    Abstract: A method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a plurality of DHM images acquired using a digital holographic microscopy system. One or more connected components are identified in each of the plurality of DHM images and one or more training white blood cell images are generated from the one or more connected components. A classifier is trained to identify a plurality of white blood cell types using the one or more training white blood cell images. The classifier may be applied to a new white blood cell image to determine a plurality of probability values, each respective probability value corresponding to one of the plurality of white blood cell types. The new white blood cell image and the plurality of probability values may then be presented in a graphical user interface.
    Type: Grant
    Filed: November 16, 2018
    Date of Patent: June 2, 2020
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Noha El-Zehiry, Shanhui Sun, Bogdan Georgescu, Lance Ladic, Ali Kamen
  • Publication number: 20200160527
    Abstract: Systems and methods are provided for evaluating an aorta of a patient. A medical image of an aorta of a patient is received. The aorta is segmented from the medical image. One or more measurement planes are identified on the segmented aorta. At least one measurement is calculated at each of the one or more measurement planes. The aorta of the patient is evaluated based on the at least one measurement calculated at each of the one or more measurement planes.
    Type: Application
    Filed: November 20, 2018
    Publication date: May 21, 2020
    Inventors: Saikiran Rapaka, Mehmet Akif Gulsun, Dominik Neumann, Jonathan Sperl, Rainer Kaergel, Bogdan Georgescu, Puneet Sharma
  • Publication number: 20200143540
    Abstract: Systems and method are described for determining a malignancy of a nodule. A medical image of a nodule of a patient is received. A patch surrounding the nodule is identified in the medical image. A malignancy of the nodule in the patch is predicted using a trained deep image-to-image network.
    Type: Application
    Filed: November 2, 2018
    Publication date: May 7, 2020
    Inventors: Sasa Grbic, Dorin Comaniciu, Bogdan Georgescu, Siqi Liu, Razvan Ionasec
  • Patent number: 10643331
    Abstract: Multi-scale deep reinforcement learning generates a multi-scale deep reinforcement model for multi-dimensional (e.g., 3D) segmentation of an object. In this context, segmentation is formulated as learning an image-driven policy for shape evolution that converges to the object boundary. The segmentation is treated as a reinforcement learning problem, and scale-space theory is used to enable robust and efficient multi-scale shape estimation. By learning an iterative strategy to find the segmentation, the learning challenges of end-to-end regression systems may be addressed.
    Type: Grant
    Filed: June 22, 2018
    Date of Patent: May 5, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Florin Cristian Ghesu, Bogdan Georgescu, Dorin Comaniciu
  • Patent number: 10643105
    Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.
    Type: Grant
    Filed: August 29, 2017
    Date of Patent: May 5, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Bogdan Georgescu, Florin Cristian Ghesu, Yefeng Zheng, Dominik Neumann, Tommaso Mansi, Dorin Comaniciu, Wen Liu, Shaohua Kevin Zhou
  • Patent number: 10627470
    Abstract: A learning-based magnetic resonance fingerprinting (MRF) reconstruction method for reconstructing an MR image of a tissue space in an MR scan subject for a particular MR sequence is disclosed. The method involves using a machine-learning algorithm that has been trained to generate a set of tissue parameters from acquired MR signal evolution without using a dictionary or dictionary matching.
    Type: Grant
    Filed: December 8, 2016
    Date of Patent: April 21, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Xiao Chen, Boris Mailhe, Qiu Wang, Shaohua Kevin Zhou, Yefeng Zheng, Xiaoguang Lu, Puneet Sharma, Benjamin L. Odry, Bogdan Georgescu, Mariappan S. Nadar
  • Patent number: 10600185
    Abstract: A method and apparatus for automated liver segmentation in a 3D medical image of a patient is disclosed. A 3D medical image, such as a 3D computed tomography (CT) volume, of a patient is received. The 3D medical image of the patient is input to a trained deep image-to-image network. The trained deep image-to-image network is trained in an adversarial network together with a discriminative network that distinguishes between predicted liver segmentation masks generated by the deep image-to-image network from input training volumes and ground truth liver segmentation masks. A liver segmentation mask defining a segmented liver region in the 3D medical image of the patient is generated using the trained deep image-to-image network.
    Type: Grant
    Filed: January 23, 2018
    Date of Patent: March 24, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Dong Yang, Daguang Xu, Shaohua Kevin Zhou, Bogdan Georgescu, Mingqing Chen, Dorin Comaniciu
  • Publication number: 20200020098
    Abstract: A method for processing medical image data comprises: inputting medical image data to a variational autoencoder configured to reduce a dimensionality of the medical image data to a latent space having one or more latent variables with latent variable values, such that the latent variable values corresponding to an image with no tissue of a target tissue type fit within one or more clusters; determining a probability that the latent variable values corresponding to the medical image data fit within the one or more clusters based on the latent variable values; and determining that a tissue of the target tissue type is present in response to a determination that the medical image data have less than a threshold probability of fitting within any of the one or more clusters based on the latent variable values.
    Type: Application
    Filed: November 3, 2017
    Publication date: January 16, 2020
    Inventors: Benjamin L. Odry, Dorin Comaniciu, Bogdan Georgescu, Mariappan S. Nadar
  • Publication number: 20190378291
    Abstract: System and methods are provided for localizing a target object in a medical image. The medical image is discretized into a plurality of images having different resolutions. For each respective image of the plurality of images, starting from a first image and progressing to a last image with the progression increasing in resolution, a sequence of actions is performed for modifying parameters of a target object in the respective image. The parameters of the target object comprise nonlinear parameters of the target object. The sequence of actions is determined by an artificial intelligence agent trained for a resolution of the respective image to optimize a reward function. The target object is localized in the medical image based on the modified parameters of the target object in the last image.
    Type: Application
    Filed: February 8, 2019
    Publication date: December 12, 2019
    Inventors: Mayalen Irene Catherine Etcheverry, Bogdan Georgescu, Sasa Grbic, Dorin Comaniciu, Benjamin L. Odry, Thomas Re, Shivam Kaushik, Bernhard Geiger, Mariappan S. Nadar
  • Publication number: 20190377978
    Abstract: Image enhancement is provided for medical diagnostic ultrasound. Knowledge-based detection of anatomy or artifact identifies locations to be enhanced. The knowledge-based detection of the locations may avoid identification of other anatomy or artifacts. The image enhancement is applied to the identified locations and not others.
    Type: Application
    Filed: August 21, 2019
    Publication date: December 12, 2019
    Inventors: Bimba Rao, Helene Houle, Bogdan Georgescu
  • Patent number: 10496729
    Abstract: A method and system for estimating tissue parameters of a computational model of organ function and their uncertainty due to model assumptions, data noise and optimization limitations is disclosed. As applied to a cardiac use-case, a patient-specific anatomical heart model is generated from medical image data of a patient. A patient-specific computational heart model is generated based on the patient-specific anatomical heart model. Patient-specific parameters and corresponding uncertainty values are estimated for at least a subset of parameters of the patient-specific computational heart model. A surrogate model is estimated for a forward model of cardiac function, and the surrogate model is applied within Bayesian inference to estimate the posterior probability density function of the parameter space of the forward model. Cardiac function for the patient is simulated using the patient-specific computational heart model.
    Type: Grant
    Filed: February 24, 2015
    Date of Patent: December 3, 2019
    Inventors: Dominik Neumann, Tommaso Mansi, Bogdan Georgescu, Ali Kamen, Dorin Comaniciu
  • Patent number: 10485510
    Abstract: A processor acquires image data from a medical imaging system. The processor generates a first model from the image data. The processor generates a computational model which includes cardiac electrophysiology and cardiac mechanics estimated from the first model. The processor performs tests on the computational model to determine outcomes for therapies. The processor overlays the outcome on an interventional image. Using interventional imaging, the first heart model may be updated/overlaid during the therapy to visualize its effect on a patient's heart.
    Type: Grant
    Filed: September 4, 2015
    Date of Patent: November 26, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Tommaso Mansi, Tiziano Passerini, Bogdan Georgescu, Ali Kamen, Helene C. Houle, Alexander Brost, Dorin Comaniciu
  • Patent number: 10483005
    Abstract: Methods and systems for estimating patient-specific cardiac electrical properties from medical image data and non-invasive electrocardiography measurements of a patient are disclosed. A patient-specific anatomical heart model is generated from medical image data of a patient. Patient-specific cardiac electrical properties are estimated by simulating cardiac electrophysiology over time in the patient-specific anatomical heart model using a computational cardiac electrophysiology model and adjusting cardiac electrical parameters based on the simulation results and the non-invasive electrocardiography measurements. A patient-specific cardiac electrophysiology model with the patient-specific cardiac electrical parameters can then be used to perform virtual cardiac electrophysiology interventions for planning and guidance of cardiac electrophysiology interventions.
    Type: Grant
    Filed: October 17, 2018
    Date of Patent: November 19, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Philipp Seegerer, Tommaso Mansi, Marie-Pierre Jolly, Bogdan Georgescu, Ali Kamen, Dorin Comaniciu, Roch Mollero, Tiziano Passerini
  • Patent number: 10482313
    Abstract: A method and system for classification of endoscopic images is disclosed. An initial trained deep network classifier is used to classify endoscopic images and determine confidence scores for the endoscopic images. The confidence score for each endoscopic image classified by the initial trained deep network classifier is compared to a learned confidence threshold. For endoscopic images with confidence scores higher than the learned threshold value, the classification result from the initial trained deep network classifier is output. Endoscopic images with confidence scores lower than the learned confidence threshold are classified using a first specialized network classifier built on a feature space of the initial trained deep network classifier.
    Type: Grant
    Filed: September 29, 2016
    Date of Patent: November 19, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Venkatesh N. Murthy, Vivek Kumar Singh, Shanhui Sun, Subhabrata Bhattacharya, Kai Ma, Ali Kamen, Bogdan Georgescu, Terrence Chen, Dorin Comaniciu
  • Patent number: 10467495
    Abstract: A method and system for anatomical landmark detection in medical images using deep neural networks is disclosed. For each of a plurality of image patches centered at a respective one of a plurality of voxels in the medical image, a subset of voxels within the image patch is input to a trained deep neural network based on a predetermined sampling pattern. A location of a target landmark in the medical image is detected using the trained deep neural network based on the subset of voxels input to the trained deep neural network from each of the plurality of image patches.
    Type: Grant
    Filed: May 11, 2015
    Date of Patent: November 5, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: David Liu, Bogdan Georgescu, Yefeng Zheng, Hien Nguyen, Shaohua Kevin Zhou, Vivek Kumar Singh, Dorin Comaniciu
  • Patent number: 10460204
    Abstract: Systems and methods for non-invasive assessment of an arterial stenosis, comprising include segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data. A hemodynamic index for the stenosis region is computed in each of the plurality of mesh candidates. It is determined whether a variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is significant with respect to a threshold associated with a clinical decision regarding the stenosis region.
    Type: Grant
    Filed: July 28, 2017
    Date of Patent: October 29, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Frank Sauer, Yefeng Zheng, Puneet Sharma, Bogdan Georgescu
  • Patent number: 10430688
    Abstract: Image enhancement is provided for medical diagnostic ultrasound. Knowledge-based detection of anatomy or artifact identifies locations to be enhanced. The knowledge-based detection of the locations may avoid identification of other anatomy or artifacts. The image enhancement is applied to the identified locations and not others.
    Type: Grant
    Filed: May 27, 2015
    Date of Patent: October 1, 2019
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Bimba Rao, Helene Houle, Bogdan Georgescu
  • Publication number: 20190295709
    Abstract: Systems and methods are provided for determining an analytic measure of a patient population. A knowledge database comprising structured patient data for a patient population is maintained. The structured patient data is generated by processing unstructured medical imaging data for the patient population using one or more machine learning algorithms. An analytic measure of the patient population is determined based on the structured patient data of the knowledge database. The analytic measure of the patient population is output.
    Type: Application
    Filed: March 18, 2019
    Publication date: September 26, 2019
    Inventors: Guillaume Chabin, Sasa Grbic, Thomas Re, Bogdan Georgescu, Afshin Ezzi, Dorin Comaniciu, Daphne Yu
  • Patent number: 10373313
    Abstract: A method and system for automated spatially-consistent multi-scale detection of anatomical landmarks in medical images is disclosed. A discrete scale-space representation of a medical image of a patient is generated. A plurality of anatomical landmarks are detected at a coarsest scale-level of the discrete scale-space representation of the medical image using a respective trained search model trained at the coarsest scale-level for each of the plurality of anatomical landmarks. Spatial coherence of the detected anatomical landmarks is enforced by fitting a learned robust shape model of the plurality of anatomical landmarks to the detected anatomical landmarks at the coarsest scale-level to robustly determine a set of the anatomical landmarks within a field-of-view of the medical image.
    Type: Grant
    Filed: September 5, 2017
    Date of Patent: August 6, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Florin Cristian Ghesu, Bogdan Georgescu, Sasa Grbic, Dorin Comaniciu