Siemens Healthcare Gmbh

Philipp Seegerer

Dorin Comaniciu

Bogdan Georgescu

Ali Kamen

Tommaso Mansi

Tiziano Passerini

Marie-Pierre Jolly

Olivier Pauly

Roch Mollero

Shaohua Kevin Zhou

Lucian Mihai Itu

Daguang Xu

Siqi Liu

Anamaria Vizitiu

Alexandru Turcea

Laszlo Lazar

Iulian Ionut Stroia

Lulian Ionut Stroia

In a method and a computer for determining a training function in order to generate annotated training images, a training image and training-image information are provided to a computer, together with an isolated item of image information that is independent of the training image. A first calculation is made in the computer by applying an image-information-processing first function to the isolated item of image information, and a second calculation is made by applying an image-information-processing second function to the training image. Adjustments to the first and second functions are made based on these calculation results, from which a determination of a training function is then made in the computer.

Method and computer for determination of a training function for generating annotated training images

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.

Semantic segmentation for cancer detection in digital breast tomosynthesis

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.

System and method for characterization of electrical properties of the heart from medical images and body surface potentials

Growing community of inventors

Fuerstenzell, Germany

Philipp Seegerer