Siemens Medical Solutions Usa, Inc.

Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

The Johns Hopkins University

Siemens Corporation

Siemens Healthineers Ag

Atilla Peter Kiraly

Carol L Novak

Benjamin L Odry

Shaohua Kevin Zhou

David D Liu

Dorin Comaniciu

Bernhard Geiger

Zhoubing Xu

Jin-hyeong Park

Bogdan Georgescu

Puneet Sharma

Ali Kamen

Hong Shen

Christopher Vincent Alvino

Michael Sarju Vaz

Shanhui Sun

Yefeng Zheng

Berthold Kiefer

Mehmet Akif Gulsun

Christophe Chefd'hotel

Kaloian Petkov

Norbert Strobel

Xiaoguang Lu

Daguang Xu

Robert Grimm

Lutz Gündel

Mingqing Chen

Chen-Rui Chou

Gunnar Krüger

Julien Christian Barbot

Tae Soo Kim

Hui Ding

Bjoern Coenen

Leo John Grady

Terrence Chen

Tommaso Mansi

Michal Sofka

Li Zhang

Saikiran Rapaka

Grzegorz Soza

Wen Wu

Noha Youssry El-Zehiry

Neil Birkbeck

Andre De Oliveira

Alexander Brost

Lutz Guendel

Clifford R Weiss

Dijia Wu

Georg Goertler

Meizhu Liu

Li Pan

Liron Yatziv

Georg Görtler

Peter Gall

Julian Ibarz

Alexander Weiss

Ihab Kamel

Jiancong Wang

Clement Jad Abi Nader

Yaron Anavi

Mathieu Chartouni

David Thivierge-Gaulin

Diran Guiliguian

David P Naidich

Jens N Kaftan

Eric Pichon

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

A method of visualizing spinal nerves includes receiving a 3D image volume depicting a spinal cord and a plurality of spinal nerves. For each spinal nerve, a 2D spinal nerve image is generated by defining a surface within the 3D volume comprising the spinal nerve. The surface is curved such that it passes through the spinal cord while encompassing the spinal nerve. Then, the 2D spinal nerve images are generated based on voxels on the surface included in the 3D volume. A visualization of the 2D spinal images is presented in a graphical user interface that allows each 2D spinal image to be viewed simultaneously.

System and method for next-generation MRI spine evaluation

In a method, a computer and a medical computer for automatic failure analysis in order to provide a cause of failure of the medical imaging apparatus during operation, input data are read into the computer that include raw data or image data, acquired by the imaging apparatus. A set of performance indicators in the input data is calculated by the computer. A trained neural network system is accessed with the calculated performance indicators, in order to provide result data that, in the case of a failure, identify a failure source.

Automatic failure detection in magnetic resonance apparatuses

To assist a physician in diagnosis of trauma involving abdominal pain, scan data representing the patient is partitioned by organ and/or region. Separate machine-learnt classifiers are provided for each organ and/or region. The classifiers are trained to indicate a likelihood of cause of the pain. By outputting results from the collection of organ and/or regions specific classifiers, the likeliest causes and associated organs and/or regions may be used by the physician to speed, confirm, or guide diagnosis of the source of abdominal pain.

Source of abdominal pain identification in medical imaging

A method of evaluating airway wall density and inflammation including: segmenting a bronchial tree to create an airway wall map; for each branch, taking a set of locations that form the wall of each branch from the map and sampling the value in a virtual non-contrast image of the bronchial tree and, given a set of samples of pre-contrast densities, computing a value to yield a bronchial wall density for each branch to yield density measures; for each branch, taking the set of locations that form the wall of each branch from the map and sampling the value in a contrast agent map of the bronchial tree and, given the set of samples of contrast agent intake, computing a value to yield a bronchial wall uptake for each branch to yield inflammation measures; and using the density and inflammation measures to determine treatment or predict outcome for a patient.

Method and system for simultaneous evaluation of airway wall density and airway wall inflammation

A computer-implemented method for deriving biopsy results in a non-invasive manner includes acquiring a plurality of training data items. Each training data item comprises non-invasive patient data and one or more biopsy derived scores associated with an individual. The method further includes extracting a plurality of features from the non-invasive patient data based on the one or more biopsy derived scores and training a predictive model to generate a predicted biopsy score based on the plurality of features and the one or more biopsy derived scores.

Virtual biopsy techniques for analyzing diseases

Systems and methods are provided for generating a visualization of a lung fissure. Medical imaging data is processed to identify a lung mesh and fissures data. The lung mesh is augmented with the fissures data and projected onto a straight plane for rendering into a concise two-dimensional image in which completeness of the lung fissure may be detected.

Visualization of lung fissures in medical imaging

Tissue is characterized using machine-learnt classification. The prognosis, diagnosis or evidence in the form of a similar case is found by machine-learnt classification from features extracted from frames of medical scan data. The texture features for tissue characterization may be learned using deep learning. Using the features, therapy response is predicted from magnetic resonance functional measures before and after treatment in one example. Using the machine-learnt classification, the number of measures after treatment may be reduced as compared to RECIST for predicting the outcome of the treatment, allowing earlier termination or alteration of the therapy.

Tissue characterization based on machine learning in medical imaging

Apparatuses and methods are provided for projecting a display of information for an x-ray device. The display of information is projected on the patient, on the tablearea or on a combination thereof. A compact projectoris provided to project the display of information on a previously unused region of the patient and table, and provides a source of diagnostic and safety information without further crowding the room. The projected display of information may be a system state, a patient state, a surgical tool position or another relevant image. A compact sensor or range scannermay be employed to capture three-dimensional distance information in the x-ray system. The three-dimensional distance information may be used to reduce distortion in the projected image, selectively darken regions of the projected image, automatically configure the x-ray system, capture inputs from the operator and improve safety in the x-ray system.

Apparatus and methods for a projection display device on X-ray imaging devices

A 2D medical image is colorized. In one approach, a deep-learnt classifier is trained to colorize from color 2D medical images. The color 2D medical images for training are cinematically rendered from slabs to add color. In another approach, a deep machine-learnt generator creates slices as if adjacent to the 2D medical image. The slices and 2D medical image form a slab, which is cinematically rendered to add color. The result is a colorized 2D medical image.

Two-dimensional cinematic medical imaging in color based on deep learning

For patient positioning for scanning, a current pose of a patient is compared to a desired pose. The desired pose may be based on a protocol or a pose of the same patient in a previous examination. Any differences in pose, such as arm position, leg position, head orientation, and/or torso orientation (e.g., laying on side, back, or stomach), are communicated. By changing the current pose of the patient to be more similar to the desired pose, a more consistent and/or registerable dataset may be acquired by scanning the patient.

Patient position control for scanning

A generative network is used for lung lobe segmentation or lung fissure localization, or for training a machine network for lobar segmentation or localization. For segmentation, deep learning is used to better deal with a sparse sampling of training data. To increase the amount of training data available, an image-to-image or generative network localizes fissures in at least some of the samples. The deep-learnt network, fissure localization, or other segmentation may benefit from generative localization of fissures.

Trained generative network for lung segmentation in medical imaging

A method and apparatus for automated prostate tumor detection and classification in multi-parametric magnetic resonance imaging (MRI) is disclosed. A multi-parametric MRI image set of a patient, including a plurality of different types of MRI images, is received. Simultaneous detection and classification of prostate tumors in the multi-parametric MRI image set of the patient are performed using a trained multi-channel image-to-image convolutional encoder-decoder that inputs multiple MRI images of the multi-parametric MRI image set of the patient and includes a plurality of output channels corresponding to a plurality of different tumor classes. For each output channel, the trained image-to image convolutional encoder-decoder generates a respective response map that provides detected locations of prostate tumors of the corresponding tumor class in the multi-parametric MRI image set of the patient.

Deep convolutional encoder-decoder for prostate cancer detection and classification

Growing community of inventors

Plainsboro, NJ, United States of America

Atilla Peter Kiraly