Siemens Healthcare Gmbh

Siemens Medical Solutions USA, Inc.

Shu Liao

Yiqiang Zhan

Xiang Sean Zhou

Gerardo Hermosillo Valadez

Yoshihisa Shinagawa

Matthias Wolf

Zhigang Peng

Fitsum Aklilu Reda

Zhennan Yan

Gerhard Kleinszig

Luca Bogoni

Marcos Salganicoff

Heiko Meyer

Martin Harder

Liang Zhao

Christian Tietjen

Jan Kretschmer

Sven Kohle

Yu Zhao

Felix Ritter

Gerardo Hermosillo-Valadez

Mingzhong Li

Yimo Gao

A method and system are for identification of at least one medical reference image. An embodiment of the method includes providing a medical representation image based on a current examination image depicting a body part of a first patient; defining a region of interest in the medical representation image; generating a feature signature, at least for the region of interest; comparing the medical representation image with a plurality of medical images of at least one second patient stored in a medical image database, based on the feature signature generated; and identifying at least one medical image in the medical image database as the at least one medical reference image, the at least one medical reference image providing a similarity degree to the medical representation image above a threshold. In an embodiment, the generating is performed using a trained machine-learning algorithm.

Searching a medical reference image

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 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

Disclosed herein is a framework for facilitating automatic planning for medical imaging. In accordance with one aspect, the framework receives first image data of a subject. One or more imaging parameters may then be derived using a geometric model and at least one reference anatomical primitive detected in the first image data. The geometric model defines a geometric relationship between the detected reference anatomical primitive and the one or more imaging parameters. The one or more imaging parameters may be presented, via a user interface, for use in acquisition, reconstruction or processing of second image data of the subject.

Automatic planning for medical imaging

Disclosed herein is a framework for segmenting articulated structures. In accordance with one aspect, the framework receives a target image, a reference image, statistical shape models, local appearance models and a learned landmark detector. The framework may automatically detect first centerline landmarks along centerlines of articulated structures in the target image using the learned landmark detector. The framework may then determine a non-rigid transformation function that registers second centerline landmarks along centerlines of articulated structures in the reference image with the first centerline landmarks. Mean shapes of the statistical shape models may then be deformed to the target image space by applying the non-rigid transformation function on the mean shapes. The framework may further search for candidate points in the mean shapes using the local appearance models. The mean shapes may be fitted to the candidate points to generate a segmentation mask.

Automatic segmentation of articulated structures

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Chester Springs, PA, United States of America

Shu Liao