Patents by Inventor MATTHIAS LENGA

MATTHIAS LENGA 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: 20240395025
    Abstract: A system (SYS) and related method for providing training data. The system is configured to receive a classification result for a class from plural pre-defined classes (Cj). The classification result is produced by a trained machine learning model (M) in response to processing an input image. A decision logic (DL) of the system is configured to analyze input data (pi,qt) comprising the received classification result value (pi) and an uncertainty value (qi) associated with the classification result value. The system outputs, per class, an associated indication whether the input image is or is not useful for re-training the model (M) in respect of the said class.
    Type: Application
    Filed: September 29, 2022
    Publication date: November 28, 2024
    Inventors: DIMITRIOS MAVROEIDIS, RICHARD VDOVJAK, AXEL SAALBACH, MATTHIAS LENGA
  • Publication number: 20240037920
    Abstract: A system and method for training a machine learning module to provide classification and localization information for an image study. The method includes receiving a current image study. The method includes applying the machine learning module to the current image study to generate a classification result including a prediction for one or more class labels for the current image study using User Interface 104 a classification module of the machine learning module. The method includes receiving, via a user interface, a user input indicating a spatial location corresponding to a predicted class label. The method includes training a localization module of the machine learning module using the user input indicating the spatial location corresponding to the predicted class label.
    Type: Application
    Filed: December 18, 2021
    Publication date: February 1, 2024
    Inventors: MATTHIAS LENGA, AXEL SAALBACH, NICOLE SCHADEWALDT, STEFFEN RENISCH, HEINRICH SCHULZ
  • Publication number: 20240021320
    Abstract: A system and method for training a deep learning network with previously read image studies to provide a prioritized worklist of unread image studies. The method includes collecting training data including a plurality of previously read image studies, each of the previously read image studies including a classification of findings and radiologist-specific data. The method includes training the deep learning neural network with the training data to predict an urgency score for reading of an unread image study.
    Type: Application
    Filed: November 11, 2021
    Publication date: January 18, 2024
    Inventors: NICOLE SCHADEWALDT, ROLF JÜRGEN WEESE, MATTHIAS LENGA, AXEL SAALBACH, STEFFEN RENISCH, HEINRICH SCHULZ
  • Publication number: 20240005484
    Abstract: A system and related method for image processing. The system comprises an input (IN) interface for receiving two segmentation maps for an input image. The two segmentation maps (11,12) obtained by respective segmentors, a first segmentor (SEG1) and a second segmentor (SEG2). The first segmentor (SEG1) implements a shape-prior-based segmentation algorithm. The second segmentor (SEG2) implements a segmentation algorithm that is not based on a shape-prior, or at least the second segmentor (SEG2) accounts for one or more shape priors at a lower weight as compared to the first segmentor (SEG1). A differentiator (DIF) configured to ascertain a difference between the two segmentation maps. The system may allow detection of abnormalities.
    Type: Application
    Filed: October 9, 2021
    Publication date: January 4, 2024
    Inventors: CHRISTIAN BUERGER, JENS VON BERG, MATTHIAS LENGA, CRISTIAN LORENZ
  • Publication number: 20230309936
    Abstract: The present invention relates to a system and a method for automatic verification of a positioning of a medical device with respect to an anatomy of a patient in a medical image. A position of a plurality of reference points in a medical image is detected. Further, a presence and a position of a medical device in the medical image is detected. An expected position of the medical device is determined based on the position of the plurality of reference points, and a measure of a correctness of the positioning of the medical device is provided based on a proximity of the position of the medical device to the expected position of the medical device. The measure of the correctness of the positioning of the medical device is provided.
    Type: Application
    Filed: August 26, 2021
    Publication date: October 5, 2023
    Inventors: AXEL SAALBACH, ILYAS SIRAZITDINOV, LEONHARD STEINMEISTER, HARALD ITTRICH, MATTHIAS LENGA, IVO MATTEO BALTRUSCHAT, MICHAEL GRASS
  • Publication number: 20230038965
    Abstract: Presented are concepts for initialising a model for model-based segmentation of an image which use specific landmarks (e.g. detected using other techniques) to initialize the segmentation mesh. Using such an approach, embodiments need not be limited to predefined model transformations, but can initialise a segmentation mesh with arbitrary shape. In this way, embodiments may provide for an image segmentation algorithm that not only delivers a robust surface-based segmentation result but also does so for strongly varying target structure variations (in terms of shape).
    Type: Application
    Filed: February 11, 2021
    Publication date: February 9, 2023
    Inventors: CHRISTIAN BUERGER, TOBIAS KLINDER, JENS VON BERG, ASTRID RUTH FRANZ, MATTHIAS LENGA, CRISTIAN LORENZ
  • Publication number: 20230005158
    Abstract: Some embodiments are directed to a segmentation of medical images. For example, a medical image may be registering to multiple atlas images after which a segmentation function may be applied. Multiple segmentation may be fused into a final overall segmentation. The atlas images may be selected on the basis of high segmentation quality or low registration quality.
    Type: Application
    Filed: December 7, 2020
    Publication date: January 5, 2023
    Inventors: MATTHIAS LENGA, TOBIAS WISSEL, ROLF JUERGEN WEESE
  • Publication number: 20220415021
    Abstract: A mechanism for creating/synthesizing realistic training data, for training a machine-learning model, using anatomical knowledge. An anatomical model can be obtained. Information from annotated training data entries (i.e. “ground truth” information), can be used to model the anatomical variation, from the obtained model, in the population of the training data. This anatomical model can then be modified, e.g. incorporating some random factors, in order to generate one or more augmented models of realistic anatomies. The augmented anatomy is then transferred from the model domain to the data entry domain to thereby generate a new data entry or data entries for training a machine-learning model. This latter process can be achieved in various ways, e.g. using GANs, such as CycleGANs and label images, or deformation vector fields.
    Type: Application
    Filed: December 7, 2020
    Publication date: December 29, 2022
    Inventors: ARNE EWALD, FRANK MICHAEL WEBER, IRINA WAECHTER-STEHLE, TOBIAS WISSEL, MATTHIAS LENGA, JOCHEN PETERS
  • Publication number: 20220398740
    Abstract: In a method of segmenting a feature in an image, an image product related to the image is provided (102) to a model trained using a machine learning process. An indication of a shape descriptor for the feature in the image is received (104) from the model, based on the image product. The indicated shape descriptor is then used (106) in a model based segmentation, MBS, to initialize the MBS and segment the feature.
    Type: Application
    Filed: October 22, 2020
    Publication date: December 15, 2022
    Inventors: MATTHIAS LENGA, CHRISTIAN BUERGER, STEFFEN RENISCH
  • Publication number: 20220142612
    Abstract: The invention provides for a method for switching between fields of view of an ultrasound probe. The method begins by obtaining an anatomical model representing a region of interest of a subject and establishing a first field of view relative to an ultrasonic probe, wherein the first field of view comprises an initial portion of the region of interest. Ultrasound data is then obtained from the first field of view by way of the ultrasonic probe and a first anatomical feature is identified within the first field of view based on the ultrasound data. A location in digital space of the first field of view relative to the anatomical model is determined based on the first anatomical feature. A second field of view is then established based on the anatomical model and the first field of view, wherein the first field of view functions as a reference field of view. The field of view is then switched from the first field of view to the second field of view.
    Type: Application
    Filed: March 13, 2020
    Publication date: May 12, 2022
    Inventors: FRANK MICHAEL WEBER, IRINA WAECHTER-STEHLE, TOBIAS WISSEL, ARNE EWALD, MATTHIAS LENGA, JOCHEN PETERS
  • Publication number: 20220101626
    Abstract: Presented are concepts for obtaining a confidence measure for a machine learning model. One such concept process input data with the machine learning model to generate a primary result. It also generate a plurality of modified instances of the input data and processes the plurality of modified instances of the input data with the machine learning model to generate a respective plurality of secondary results. A confidence measure relating to the primary result is determined based on the secondary results.
    Type: Application
    Filed: January 21, 2020
    Publication date: March 31, 2022
    Inventors: MATTHIAS LENGA, RAFAEL WIEMKER, TOBIAS KLINDER, MARTEN BERGTHOLDT, HEIKE CAROLUS