Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Healthineers Ag

Mingqing Chen

Shaohua Kevin Zhou

Daguang Xu

Zhoubing Xu

Dorin Comaniciu

Yefeng Zheng

Dong Yang

Gareth Funka-Lea

Bogdan Georgescu

Jan Boese

Mehmet Akif Gulsun

Jin-hyeong Park

Tae Soo Kim

Puneet Sharma

Shanhui Sun

Atilla Peter Kiraly

David D Liu

Günter Lauritsch

Xiaoguang Lu

Christopher Rohkohl

Kerstin Mueller

Hui Ding

Tao Xiong

He Zhang

Terrence Chen

Vivek Kumar Singh

Sasa Grbic

Yao-Jen Chang

Shun P Miao

Kai Ma

Andreas Krauss

Jan Kretschmer

Jiangping Wang

Max Schöbinger

Guenter Lauritsch

Sebastian Seifert

Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

Method and system for artificial intelligence based medical image segmentation

Systems and methods are provided for performing medical imaging analysis. Input medical imaging data is received for performing a particular one of a plurality of medical imaging analyses. An output that provides a result of the particular medical imaging analysis on the input medical imaging data is generated using a neural network trained to perform the plurality of medical imaging analyses. The neural network is trained by learning one or more weights associated with the particular medical imaging analysis using one or more weights associated with a different one of the plurality of medical imaging analyses. The generated output is outputted for performing the particular medical imaging analysis.

Hierarchical learning of weights of a neural network for performing multiple analyses

Methods and apparatus for automated medical image analysis using deep learning networks are disclosed. In a method of automatically performing a medical image analysis task on a medical image of a patient, a medical image of a patient is received. The medical image is input to a trained deep neural network. An output model that provides a result of a target medical image analysis task on the input medical image is automatically estimated using the trained deep neural network. The trained deep neural network is trained in one of a discriminative adversarial network or a deep image-to-image dual inverse network.

Adversarial and dual inverse deep learning networks for medical image analysis

A method and apparatus for automated liver segmentation in a 3D medical image of a patient is disclosed. A 3D medical image, such as a 3D computed tomography (CT) volume, of a patient is received. The 3D medical image of the patient is input to a trained deep image-to-image network. The trained deep image-to-image network is trained in an adversarial network together with a discriminative network that distinguishes between predicted liver segmentation masks generated by the deep image-to-image network from input training volumes and ground truth liver segmentation masks. A liver segmentation mask defining a segmented liver region in the 3D medical image of the patient is generated using the trained deep image-to-image network.

Automatic liver segmentation using adversarial image-to-image network

A method and apparatus for deep learning based automatic bone removal in medical images, such as computed tomography angiography (CTA) volumes, is disclosed. Bone structures are segmented in a 3D medical image of a patient by classifying voxels of the 3D medical image as bone or non-bone voxels using a deep neural network trained for bone segmentation. A 3D visualization of non-bone structures in the 3D medical image is generated by removing voxels classified as bone voxels from a 3D visualization of the 3D medical image.

Deep learning based bone removal in computed tomography angiography

In scan data retrieval, a mesh is fit () to surface data of a current patient, such as data from an optical or depth sensor (). Meshes are also fit () to medical scan data, such as fitting () to skin surface segments of computed tomography data. The meshes or parameters derived from the meshes may be more efficiently compared () to identify () a previous patient with similar body shape and/or size. The scan configuration () for that patient, or that patient as altered to account for differences from the current patient, is used. In some embodiments, the parameter vector used for searching () includes principle component analysis coefficients. In further embodiments, the principle component analysis coefficients may be projected to a more discriminative space using metric learning.

Scan data retrieval with depth sensor data

A method and apparatus for automated vertebra localization and identification in a 3D computed tomography (CT) volumes is disclosed. Initial vertebra locations in a 3D CT volume of a patient are predicted for a plurality of vertebrae corresponding to a plurality of vertebra labels using a trained deep image-to-image network (DI2IN). The initial vertebra locations for the plurality of vertebrae predicted using the DI2IN are refined using a trained recurrent neural network, resulting in an updated set of vertebra locations for the plurality of vertebrae corresponding to the plurality of vertebrae labels. Final vertebra locations in the 3D CT volume for the plurality of vertebrae corresponding to the plurality of vertebra labels are determined by refining the updated set of vertebra locations using a trained shape-basis deep neural network.

Deep image-to-image recurrent network with shape basis for automatic vertebra labeling in large-scale 3D CT volumes

The present embodiments relate to detecting multiple landmarks in medical images. By way of introduction, the present embodiments described below include apparatuses and methods for detecting landmarks using hierarchical feature learning with end-to-end training. Multiple neural networks are provided with convolutional layers for extracting features from medical images and with a convolutional layer for learning spatial relationships between the extracted features. Each neural network is trained to detect different landmarks using a different resolution of the medical images, and the convolutional layers of each neural network are trained together with end-to-end training to learn appearance and spatial configuration simultaneously. The trained neural networks detect multiple landmarks in a test image iteratively by detecting landmarks at different resolutions, using landmarks detected a lesser resolutions to detect additional landmarks at higher resolutions.

Multiple landmark detection in medical images based on hierarchical feature learning and end-to-end training

A method and apparatus for learning based classification of vascular branches to distinguish falsely detected branches from true branches is disclosed. A plurality of overlapping fixed size branch segments are sampled from branches of a detected centerline tree of a target vessel extracted from a medical image of a patient. A plurality of 1D profiles are extracted along each of the overlapping fixed size branch segments. A probability score for each of the overlapping fixed size branch segments is calculated based on the plurality of 1D profiles extracted for each branch segment using a trained deep neural network classifier. The trained deep neural network classifier may be a convolutional neural network (CNN) trained to predict a probability of a branch segment being fully part of a target vessel based on multi-channel 1D input. A final probability score is assigned to each centerline point in the branches of the detected centerline tree based on the probability scores of the overlapping branch segments containing that centerline point. The branches of the detected centerline tree of the target vessel are pruned based on the final probability scores of the centerline points.

Method and system for machine learning based classification of vascular branches

The left ventricle epicardium is estimated in medical diagnostic imaging. C-arm x-ray data is used to detect an endocardium at different phases. The detected endocardium at the different phases is compared to sample endocardiums at different phases. The sample endocardiums have corresponding sample epicardiums. The transformation between the most similar sample endocardium or endocardiums over time and the detected endocardium over time is applied to the corresponding sample epicardium or epicardiums. The transformed sample epicardium over time is the estimated epicardium over time for the C-arm x-ray data.

Left ventricle epicardium estimation in medical diagnostic imaging

Background information is subtracted from projection data in medical diagnostic imaging. The background is removed using data acquired in a single rotational sweep of a C-arm. The removal may be by masking out a target, leaving the background, in the data as constructed into a volume. For subtraction, the masked background information is projected to a plane and subtracted from the data representing the plane.

Subtraction of projection data in medical diagnostic imaging

A method and system for estimating 3D cardiac motion from a single C-arm angiography scan is disclosed. An initial 3D volume is reconstructed from a plurality of 2D projection images acquired in a single C-arm scan. A static mesh is extracted by segmenting an object in the initial 3D volume. The static mesh is projected to each of the 2D projection images. A cardiac phase is determined for each of the 2D projection images. A deformed mesh is generated for each of a plurality of cardiac phases based on a 2D contour of the object and the projected mesh in each of the 2D projection images of that cardiac phase.

Method and system for 3D cardiac motion estimation from single scan of C-arm angiography

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Plainsboro, NJ, United States of America

Mingqing Chen