Siemens Medical Solutions Usa, Inc.

Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Corporate Research, Inc.

Siemens Corporation

Siemens Healthineers Ag

Siemens Information Systems, Ltd.

Xiang Sean Zhou

Dorin Comaniciu

Yiqiang Zhan

Arun Krishnan

Bogdan Georgescu

Alok Gupta

Gerardo Hermosillo Valadez

Zhigang Peng

Luca Bogoni

R Bharat Rao

Marcos Salganicoff

Sriram Krishnan

Shu Liao

Fitsum Aklilu Reda

Maneesh Dewan

Diane S Paine

Bhavani Duggirala

Yoshihisa Shinagawa

Matthias Wolf

Anna Jerebko

Zhuowen Tu

Venkat Raghavan Ramamurthy

Shaohua Kevin Zhou

Jing Xiao

Visvanathan Ramesh

Berthold Kiefer

Matthias Fenchel

Gerhard Kleinszig

Adrian Barbu

Michael Kelm

Liang Zhao

Parmeet Singh Bhatia

Xiaolei Sharon Huang

Christian P Schultz

Vaclav Potesil

Gerardo Hermosillo-Valadez

Zhennan Yan

Yefeng Zheng

Rainer Graumann

Swen Campagna

Ying Zhu

Michael G Cannon

Ute Feuerlein

Martin Harder

Vikas Raykar

Kazunori Okada

Martin Huber

Senthil Periaswamy

Thomas Friese

Le Lu

Shashi Kumar

Jun Ma

Tilo Christ

Toshiro Kubota

Michael Rommel

Benedikt Krueger

Thomas Gossler

Yu Zhao

Yuanhsi (Tony) Chen

Bohyung Han

Moritz Uebler

Friedrich Hegendoerfer

Zhao Yi

Haiying Guan

Wei Hong

Allen Dutton

Anlei Dong

Markus Kirchner

Tobias Hartmann

Frank Steinmetz

Robert Soellner

Eric Albrecht

Yimo Gao

Yimo Tao

Jiamin Liu

Mert Dikmen

Jörg Freund

Karthikeyan Kaliyamoorthi

Mingzhong Li

Joerg Freund

A method is for inserting a further data block into a first ledger, the first ledger including data blocks. In an embodiment, the method includes receiving a further medical dataset via an interface; determining the further data block via a calculation unit. The further data block includes the further medical dataset and a further link information. The further link information includes a hash of at least one of the data blocks of the first ledger. Finally, in an embodiment the method includes inserting the further data block into the first ledger via the calculation unit.

Inserting a further data block into a first ledger

A framework for visualization based on healthcare guidelines is described herein. In accordance with one aspect, a decision graph is generated based on healthcare guidelines. The nodes of the decision graph are populated with patient records. The framework may perform data analytics on patient records associated with at least one of the nodes to generate results, and further generate a visualization of the results.

Data-enriched electronic healthcare guidelines for analytics, visualization or clinical decision support

A framework diagnostic test planning is described herein. In accordance with one aspect, the framework receives data representing one or more sample patients, diagnostic tests administered to the one or more sample patients, diagnostic test results and confirmed medical conditions associated with the administered diagnostic tests. The framework trains one or more classifiers based on the data to identify diagnostic test plans from the diagnostic tests. The one or more classifiers may then be applied to current patient data to generate a diagnostic test plan for a given patient.

Diagnostic test planning using machine learning techniques

A framework for patient management based on anatomic measurements is described herein. In accordance with one aspect, patient records are clustered into a set of sub-populations based on first anatomic measurements and characteristics extracted from first patient data associated with a population of patients. A representative sub-population similar to a patient may be determined from the set of sub-populations based on the patient data of the patient. A report that presents the second anatomic measurements associated with the patient in relation to corresponding first anatomic measurements associated with the representative sub-population may then be generated.

Patient management based on anatomic measurements

A framework for cross-modality image synthesis. A first and second model may be trained using respective first and second pairs of complementary images and corresponding first and second ground truth images that represent first and second views of a region of interest. The first and second pairs of complementary images may be acquired by a first modality and the first and second ground truth images may be acquired by a second modality. A combinational network may further be trained to combine features from the first and second models. At least one synthetic second modality image may then be generated by passing current images through the trained first or second model and the combinational network, wherein the current images are acquired by the first modality and represent the first or second view of the region of interest.

Cross-modality image synthesis

A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.

Automated measurement based on deep learning

A framework for coarse orientation detection in image data. In accordance with one aspect, the framework trains a learning structure to recognize a coarse orientation of the anatomical structure of interest based on training images. The framework may then pass one or more current images through the trained learning structure to generate a coarse orientation of the anatomical structure of interest. The framework then outputs the generated coarse orientation of the anatomical structure of interest.

Coarse orientation detection in image data

A framework for facilitating visualization, including localizing at least one anatomical structure of interest in image data. The structure of interest is then highlighted by reformatting the image data by mapping landmarks associated with the structure of interest to corresponding points along a contour of a geometric shape and warping the image data based on the mapped landmarks. The resulting reformatted image data is rendered for display to a user.

Visualization with anatomical intelligence

A framework for pedicle screw positioning is described herein. In accordance with one aspect, the framework segments at least one vertebra of interest in image data. The framework then automatically determines a pedicle region within the segmented vertebra of interest, and a safe region within the segmented vertebra of interest. An optimal insertion path passing through the pedicle region may then be generated within the safe region.

Image-based pedicle screw positioning

A framework for automatic retrieval of medical images. In accordance with one aspect, the framework detects patches in a query image volume that contain at least a portion of an anatomical region of interest by using a first trained classifier. The framework determines disease probabilities by applying a second trained classifier to the detected patches, and selects, from the patches, a sub-set of informative patches with disease probabilities above a pre-determined threshold value. For a given patch from the sub-set of informative patches, the framework retrieves, from a database, patches that are most similar to the given image. Image volumes associated with the retrieved patches are then retrieved from the database. A report based on the retrieved image volumes may then be generated and presented.

Automatic medical image retrieval

A method for automatically detecting a region of interest in a digital medical image, comprising over-segmenting the image into a plurality of superpixels through use of an over-segmentation algorithm; for each pair of neighboring superpixels in the plurality of superpixels, computing, through a machine learning algorithm, the probability of each pair being in one of three predetermined classes; for each superpixel in the plurality of superpixels, computing a probability of the superpixel being in the region of interest; generating an edge map from computing each pixel's value based on the computed superpixel probabilities; applying an extended Hough transform to the generated edge map to generate a Hough parameter counting space; determining the optimal quadrilateral in the Hough parameter counting space by excluding false positive edges; and designating the region of interest as being within the boundary of the determined optimal quadrilateral.

Automated region of interest detection using machine learning and extended Hough transform

Disclosed herein is a framework for facilitating image processing. In accordance with one aspect, the framework receives first image data acquired by a first modality and one or more articulated models. The one or more articulated models may include at least one section image acquired by the first modality and aligned with a local image acquired by a second modality. The framework may align an anatomical region of the first image data with the section image and non-rigidly register a first region of interest extracted from the section image with a second region of interest extracted from the aligned anatomical region. To generate a segmentation mask of the anatomical region, the registered first region of interest may be inversely mapped to a subject space of the first image data.

Anatomy aware articulated registration for image segmentation

A framework for anatomy orientation detection is described herein. In accordance with one aspect, a pre-trained regressor is applied to appearance features of the image volume to predict a colatitude of the structure of interest. An optimal longitude corresponding to the predicted colatitude is then determined. In response to the colatitude being more than a pre-determined threshold, the image volume is re-oriented based on the predicted colatitude and the optimal longitude, and the predicted colatitude and optimal longitude determination is repeated for the re-oriented image volume.

Detection of anatomy orientation using learning-based regression

Disclosed herein is a framework for facilitating synchronized image navigation. In accordance with one aspect, at least first and second medical images are received. A non-linear mapping between the first and second medical images is generated. A selection of a given location in the first medical image is received in response to a user's navigational operation. Without deforming the second medical image, a target location in the second medical image is determined by using the non-linear mapping. The target location corresponds to the given location in the first medical image. An optimized deformation-free view of the second medical image is generated based at least in part on the target location. While the user performs navigational operations on the first medical image, the framework repeatedly receives the selection of the given location, determines the target location using the non-linear mapping, and generates the optimized deformation-free view of the second medical image based at least in part on the target location.

Synchronized navigation of medical images

A method for bolus tracking includes acquiring one or more baseline images. One or more trigger regions are automatically established within the baseline images. A bolus is administered. The automatically established trigger regions are monitored for bolus arrival at the one or more trigger regions. Bolus arrival at a volume of interest is forecasted based on the bolus arrival at the one or more trigger regions. A diagnostic scan of the volume of interest is acquired at the forecasted time.

System and method for automatic trigger-ROI detection and monitoring during bolus tracking

Growing community of inventors

Exton, PA, United States of America

Xiang Sean Zhou