Patents by Inventor Iulian Ionut STROIA

Iulian Ionut STROIA 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).

  • Semantic segmentation for cancer detection in digital breast tomosynthesis
    Patent number: 10779785
    Abstract: A method, apparatus and non-transitory computer readable medium are for segmenting different types of structures, including cancerous lesions and regular structures like vessels and skin, in a digital breast tomosynthesis (DBT) volume. In an embodiment, the method includes: pre-classification of the DBT volume in dense and fatty tissue and based on the result; localizing a set of structures in the DBT volume by using a multi-stream deep convolutional neural network; and segmenting the localized structures by calculating a probability for belonging to a specific type of structure for each voxel in the DBT volume by using a deep convolutional neural network for providing a three-dimensional probabilistic map.
    Type: Grant
    Filed: July 12, 2018
    Date of Patent: September 22, 2020
    Assignee: SIEMENS HEALTHCARE GMBH
    Inventors: Lucian Mihai Itu, Laszlo Lazar, Siqi Liu, Olivier Pauly, Philipp Seegerer, Iulian Ionut Stroia, Alexandru Turcea, Anamaria Vizitiu, Daguang Xu, Shaohua Kevin Zhou
  • SEMANTIC SEGMENTATION FOR CANCER DETECTION IN DIGITAL BREAST TOMOSYNTHESIS
    Publication number: 20190015059
    Abstract: A method, apparatus and non-transitory computer readable medium are for segmenting different types of structures, including cancerous lesions and regular structures like vessels and skin, in a digital breast tomosynthesis (DBT) volume. In an embodiment, the method includes: pre-classification of the DBT volume in dense and fatty tissue and based on the result; localizing a set of structures in the DBT volume by using a multi-stream deep convolutional neural network; and segmenting the localized structures by calculating a probability for belonging to a specific type of structure for each voxel in the DBT volume by using a deep convolutional neural network for providing a three-dimensional probabilistic map.
    Type: Application
    Filed: July 12, 2018
    Publication date: January 17, 2019
    Applicant: Siemens Healthcare GmbH
    Inventors: Lucian Mihai ITU, Laszlo LAZAR, Siqi LIU, Olivier PAULY, Philipp SEEGERER, Iulian Ionut STROIA, Alexandru TURCEA, Anamaria VIZITIU, Daguang XU, Shaohua Kevin ZHOU