Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Product Lifecycle Management Software Inc.

Siemens Industry Software Gmbh

Daguang Xu

Shaohua Kevin Zhou

Dorin Comaniciu

Bogdan Georgescu

David D Liu

Mingqing Chen

Zhoubing Xu

Yefeng Zheng

Li Zhang

Sasa Grbic

Dong Yang

Tommaso Mansi

Shun P Miao

Julian Krebs

Ali Kamen

Shanhui Sun

Vivek Kumar Singh

Bernhard Geiger

Rui Liao

Jin-hyeong Park

Florin Cristian Ghesu

S Kevin Zhou

Pierre De Tournemire

Puneet Sharma

Atilla Peter Kiraly

Mehmet Akif Gulsun

Xiaoguang Lu

Noha Youssry El-Zehiry

Chris Schwemmer

Olivier Pauly

Siqi Liu

Nathan S Lay

Vincent Ordy

Tae Soo Kim

Tao Xiong

Hui Ding

Philipp Wolber

Thomas Mertelmeier

Lucian Mihai Itu

Carol L Novak

Anna Jerebko

Philipp Hoelzer

Jan Kretschmer

Julia Wicklein

Ying Chi

Philipp Seegerer

Anamaria Vizitiu

Laszlo Lazar

Alexandru Turcea

Nora Hünemohr

Han Xiao

Pavlo Dyban

He Zhang

Iulian Ionut Stroia

Lulian Ionut Stroia

Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Method and system for image registration using an intelligent artificial agent

The user is to be informed of the reliability of the machine-learned model based on the current input relative to the training data used to train the model or the model itself. In a medical situation, the data for a current patient is compared to the training data used to train a prediction model and/or to a decision function of the prediction model. The comparison indicates the training content relative to the current patient, so provides a user with information on the reliability of the prediction for the current situation. The indication deals with the variation of the data of the current patient from the training data or relative to the prediction model, allowing the user to see how well trained the predication model is relative to the current patient. This indication is in addition to any global confidence output through application of the prediction model to the data of the current patient.

Characterization of amount of training for an input to a machine-learned network

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 automatically performing medical image analysis tasks using deep image-to-image network (DI2IN) learning. An input medical image of a patient is received. An output image that provides a result of a target medical image analysis task on the input medical image is automatically generated using a trained deep image-to-image network (DI2IN). The trained DI2IN uses a conditional random field (CRF) energy function to estimate the output image based on the input medical image and uses a trained deep learning network to model unary and pairwise terms of the CRF energy function. The DI2IN may be trained on an image with multiple resolutions. The input image may be split into multiple parts and a separate DI2IN may be trained for each part. Furthermore, the multi-scale and multi-part schemes can be combined to train a multi-scale multi-part DI2IN.

Deep image-to-image network learning for medical image analysis

A method, apparatus and non-transitory computer readable medium are for segmenting different types of structures, including cancerous lesions and regular structures like vessels and skin, in a digital breast tomosynthesis (DBT) volume. In an embodiment, the method includes: pre-classification of the DBT volume in dense and fatty tissue and based on the result; localizing a set of structures in the DBT volume by using a multi-stream deep convolutional neural network; and segmenting the localized structures by calculating a probability for belonging to a specific type of structure for each voxel in the DBT volume by using a deep convolutional neural network for providing a three-dimensional probabilistic map.

Semantic segmentation for cancer detection in digital breast tomosynthesis

Embodiments can provide a method for atlas-based contouring, comprising constructing a relevant atlas database; selecting one or more optimal atlases from the relevant atlas database; propagating one or more atlases; fusing the one or more atlases; and assessing the quality of one or more propagated contours.

Atlas-based contouring of organs at risk for radiation therapy

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 computer-implemented method for identifying features in 3D image volumes includes dividing a 3D volume into a plurality of 2D slices and applying a pre-trained 2D multi-channel global convolutional network (MC-GCN) to the plurality of 2D slices until convergence. Following convergence of the 2D MC-GCN, a plurality of parameters are extracted from a first feature encoder network in the 2D MC-GCN. The plurality of parameters are transferred to a second feature encoder network in a 3D Anisotropic Hybrid Network (AH-Net). The 3D AH-Net is applied to the 3D volume to yield a probability map;. Then, using the probability map, one or more of (a) coordinates of the objects with non-maximum suppression or (b) a label map of objects of interest in the 3D volume are generated.

3D anisotropic hybrid network: transferring convolutional features from 2D images to 3D anisotropic volumes

A computer-implemented method for generating contours of anatomy based on user click points includes a computer displaying an image comprising an anatomical structure and receiving a first user selection of a first click point at a first position on an outward facing edge of the anatomical structure. The computer applies a contour inference algorithm to generate an inferred contour around the outward facing edge based on the first position. Following generation of the inferred contour, the computer receives a second user selection of a second click point at a second position on the image. Then, the computer creates a visual indicator on a segment of the inferred contour between the first position and the second position as indicative of the user's confirmation of accuracy of the segment.

Intelligent contouring of anatomy with structured user click points

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Princeton, NJ, United States of America

Daguang Xu