Abstract: The invention provides for an artificial turf infill (602) for an artificial turf, wherein the artificial turf infill consists of granules (101), wherein the granules are made from unfoamed material, wherein the granules comprise a compostable polymer selected from the group consisting of polylactic acid (PLA), thermoplastic copolyester elastomer (TPC), polybutylene succinate (PBS), poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), polyhydroxybutyrate (PHB), polyhydroxyalkanoate (PHA), polybutylene adipate terephthalate (PBAT), a derivative thereof or a mixture thereof; a filler material; and an oil.

Abstract: A method and apparatus for detecting vascular landmarks in a 3D image volume, such as a CT volume, is disclosed. One or more guide slices are detected in a 3D image volume. A set of landmark candidates for multiple target vascular landmarks are then detected based on the guide slices. A node potential value for each landmark candidate is generated based on an error value determined using spatial histogram-based error regression, and edge potential values for pairs of landmark candidates are generated based on a bifurcation analysis of the image volume using vessel tracing. The optimal landmark candidate for each target landmark is then determined using a Markov random field model based on the node potential values and the edge potential values.

Abstract: A computer network system and a method are disclosed for accessing a dose image database for medical imaging using ionizing radiation via an imaging device. An embodiment includes a multiplicity of imaging devices connected via a network connection to the dose image database, each imaging device including a transmit unit to transmit the images captured by the imaging device to the dose image database. An allocation between an image or a class of images, a dose measurement value, an image quality measurement value and optionally further medical metadata are stored in the dose image database. A control unit is included to exchange date with the dose image database and to generate control variables automatically while accessing the dose image database depending on at least one selection parameter to be defined, the parameters being forwarded via a network connection to at least one imaging device to control the imaging.

Abstract: A method and apparatus for hierarchical parsing and semantic navigation of a full or partial body computed tomography CT scan is disclosed. In particular, organs are segmented and anatomic landmarks are detected in a full or partial body CT volume. One or more predetermined slices of the CT volume are detected. A plurality of anatomic landmarks and organ centers are then detected in the CT volume using a discriminative anatomical network, each detected in a portion of the CT volume constrained by at least one of the detected slices. A plurality of organs, such as heart, liver, kidneys, spleen, bladder, and prostate, are detected in a sense of a bounding box and segmented in the CT volume, detection of each organ bounding box constrained by the detected organ centers and anatomic landmarks. Organ segmentation is via a database-guided segmentation method.

Abstract: A method is disclosed for assigning measurement data of a body of a patient to information data related to the same body. The method includes providing annotated measurement data, providing annotated information data, translating the first group of annotations into a first group of universal annotations and the second group of annotations into a second group of universal medical annotations in a universal annotation ontology management system, comparing the universal annotations of the first group of universal annotations with the universal annotations of the second group of universal annotations, and assigning the universal annotations to each other. An assignment system and a universal annotation ontology management system are also disclosed.

Abstract: Methods and device are disclosed for data retrieval. At first images and reports are analyzed by respective parser units to detect both structures and text passages that are related to respective structures and text passages of a knowledge data-base. The detected structures and text passages are stored together with a unique resource located that identifies the respective structure and/or text passage at the knowledge database in a semantic annotation database. In addition a feature extraction can be performed to provide specific features of the images and/or regions of the images, whereby the features are stored in an image feature database. Finally an input query can ask questions that are used to provide a result to the query based on the semantic annotation database and the image feature database. The methods and devices may be used for data preparation and data retrieval of medical images and associated medical reports.

Abstract: A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.

Abstract: Methods and a system are disclosed for providing a mechanism for searching and extracting three-dimensional regions of interest in image archives. In at least one embodiment, a textual search query serves as input and is processed in order to provide a newly generated query-specific volume image, including computed sub-volumes covering the anatomical structure the query refers to. The query is input via a graphical search interface and the sub-volumes are outputted also via the graphical search interface.

Abstract: The present embodiments relate to methods for comparing medical data. One method includes the acts of transmitting first medical data records from a first network to a central data storage; transmitting second medical data records from a second network to the central data storage; extracting first key data from the first medical data records in the central data storage; extracting second key data from the second medical data records in the central data storage; and calculating comparison values based on the first key data and the second key data.

Abstract: A computer network system and a method are disclosed for accessing a dose image database for medical imaging using ionizing radiation via an imaging device. An embodiment includes a multiplicity of imaging devices connected via a network connection to the dose image database, each imaging device including a transmit unit to transmit the images captured by the imaging device to the dose image database. An allocation between an image or a class of images, a dose measurement value, an image quality measurement value and optionally further medical metadata are stored in the dose image database. A control unit is included to exchange date with the dose image database and to generate control variables automatically while accessing the dose image database depending on at least one selection parameter to be defined, the parameters being forwarded via a network connection to at least one imaging device to control the imaging.

Abstract: Methods and a system are disclosed for providing a mechanism for searching and extracting three-dimensional regions of interest in image archives. In at least one embodiment, a textual search query serves as input and is processed in order to provide a newly generated query-specific volume image, including computed sub-volumes covering the anatomical structure the query refers to. The query is input via a graphical search interface and the sub-volumes are outputted also via the graphical search interface.

Abstract: A method and system for fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches.

Abstract: A rendering device, a control method for a graphical user interface and a computer software product are disclosed. The rendering device includes an input interface, a parser interface and a retrieval unit, designed for the automatic generation of a search function for retrieval from accessible databases by reference to a specific semantic identifier, in order to acquire reference data, in combination with control data, for display on the graphical user interface.

Abstract: Methods and device are disclosed for data retrieval. At first images and reports are analyzed by respective parser units to detect both structures and text passages that are related to respective structures and text passages of a knowledge database. The detected structures and text passages are stored together with a unique resource located that identifies the respective structure and/or text passage at the knowledge database in a semantic annotation database. In addition a feature extraction can be performed to provide specific features of the images and/or regions of the images, whereby the features are stored in an image feature database. Finally an input query can ask questions that are used to provide a result to the query based on the semantic annotation database and the image feature database. The methods and devices may be used for data preparation and data retrieval of medical images and associated medical reports.

Abstract: A method is disclosed for assigning measurement data of a body of a patient to information data related to the same body. The method includes providing annotated measurement data, providing annotated information data, translating the first group of annotations into a first group of universal annotations and the second group of annotations into a second group of universal medical annotations in a universal annotation ontology management system, comparing the universal annotations of the first group of universal annotations with the universal annotations of the second group of universal annotations, and assigning the universal annotations to each other. An assignment system and a universal annotation ontology management system are also disclosed.

Abstract: Methods and device are disclosed for data retrieval. At first images and reports are analyzed by respective parser units to detect both structures and text passages that are related to respective structures and text passages of a knowledge data-base. The detected structures and text passages are stored together with a unique resource located that identifies the respective structure and/or text passage at the knowledge database in a semantic annotation database. In addition a feature extraction can be performed to provide specific features of the images and/or regions of the images, whereby the features are stored in an image feature database. Finally an input query can ask questions that are used to provide a result to the query based on the semantic annotation database and the image feature database. The methods and devices may be used for data preparation and data retrieval of medical images and associated medical reports.

Abstract: A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.

Abstract: A method and a system are disclosed for image annotation of images, in particular two- and three-dimensional medical images. In at least one embodiment, the image annotation system includes an image parser which parses images retrieved from an image database or provided by an image acquisition apparatus and segments each image into image regions. The image can be provided by any kind of image acquisition apparatus such as a digital camera an x-ray apparatus, a computer tomograph or a magnetic resonance scanning apparatus. Each segmented image regions is annotated automatically with annotation data and stored in an annotation database. In at least one embodiment, the system includes at least one user terminal which loads at least one selected image from said image database and retrieved the corresponding annotation data of all segmented image regions of said image from said annotation database for further annotation of the image.

Abstract: A method and system for fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches.

Abstract: A method and apparatus for detecting vascular landmarks in a 3D image volume, such as a CT volume, is disclosed. One or more guide slices are detected in a 3D image volume. A set of landmark candidates for multiple target vascular landmarks are then detected based on the guide slices. A node potential value for each landmark candidate is generated based on an error value determined using spatial histogram-based error regression, and edge potential values for pairs of landmark candidates are generated based on a bifurcation analysis of the image volume using vessel tracing. The optimal landmark candidate for each target landmark is then determined using a Markov random field model based on the node potential values and the edge potential values.