Siemens Aktiengesellschaft

Other

Siemens Medical Solutions USA, Inc.

Siemens Corporate Research, Inc.

Christian P Schultz

Dorin Comaniciu

Xiang Sean Zhou

Sebastian Schmidt

Justinian Rosca

Heiko Claussen

Fred S Azar

Zhuowen Tu

Ravi Teja Seethamraju

Wolfgang Strob

Nazif Cihan Tas

Jochem Knoche

Steven A Russell

Carolyn Joiner

Mukesh G Harisinghani

Anlei Dong

A patient monitoring and intervention system, comprises an interface for receiving data representing multiple different parameters from multiple different sensors, comprising sensors in a patient bed and attached to a patient including, a heart rate sensor, a respiration sensor and a pressure sensor indicating bed pressure points. A learning processor determines a normal range for a set of the different received patient parameters for the patient by recording the patient parameter values over a time period and analyzing the recorded parameter values to determine their range. A data processor determines if the set of different received patient parameters exceeds the determined normal range and in response to this determination and in response to the type of parameters in the set and medical record information of the patient, initiates adjustment of a patient bed and at least one of, (a) changes medication administered to a patient and (b) alerts a worker of the patient parameter change.

Learning patient monitoring and intervention system

A system detects malignant lymph nodes using an MR imaging device, for, in the absence of a contrast agent, acquiring in a patient anatomical volume of interest including lymph nodes, (a) a first image using a variable flip angle, lymph node enhanced contrast, MR image acquisition process, (b) a second image using a susceptibility weighting imaging acquisition process and (c) a third image using a diffusion weighting imaging acquisition process. In the presence of a contrast agent absorbed by benign lymph nodes, MR imaging device acquires in the patient anatomical volume of interest, (d) a fourth image using a susceptibility weighting imaging acquisition process. A display processor processes data representing the first, second, third and fourth images for display of malignant and benign lymph nodes on a reproduction device.

System for detecting malignant lymph nodes using an MR imaging device

An optical scanning device is provided. The optical scanning device an excitation light source that is operable to illuminate the object to be scanned. An image detector is operable to detect excited emission rays through illumination of the object to be scanned by the excitation light source and imaging optics that dictate a focal distance. The image detector detects a scan image of the emission radiation of the object to be scanned with the highest level of image sharpness if the focal distance is maintained. A control device includes a distance sensor through which a distance between an optical scanning device and the object to be scanned can be measured, wherein maintenance of the focal distance as the function of the measured distance can be checked by the control device.

Optical scanning device having a distance sensor and a determined focal distance for scanning objects

A method for segmenting a digitized image includes providing a digitized image comprising a plurality of intensities defined on a lattice of pixels, initializing a Bayesian prior probability and likelihood function for segmenting said image, decomposing said image into a union of a plurality of subspaces wherein each subspace is a product of k-partitions, and distributing a plurality Bayesian posterior probability functions over said subspaces, updating said Bayesian posterior probability functions via a data-driven Markov-chain, and selecting a subset of said Bayesian posterior probability functions that approximate a true Bayesian posterior probability function.

System and method for anatomical structure parsing and detection

In a method of 3D object detection, a learning procedure is used for feature selection from a feature set based on an annotated image-volume database, generating a set of selected features. A classifier is built using a classification scheme to distinguish between an object location and a non-object location and using the set of selected features. The classifier is applied at a candidate volume to determine whether the candidate volume contains an object of interest.

Methods and systems for 3D object detection using learning

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Beverly, MA, United States of America

Christian P Schultz