International Business Machines Corporation

Other

Koninklijke Philips Corporation N.V.

Guerbet

Arkadiusz Sitek

Yiting Xie

Benedikt Werner Graf

Ben Graf

Mark D Bronkalla

Amin Katouzian

Giovanni John Jacques Palma

Paul Dufort

Omid Bonakdar Sakhi

Pedro Luis Esquinas Fernandez

Dana Levanony

Yi-Qing Wang

Thomas Binder

Marwan Sati

Lilla Boroczky

Ahmed El Harouni

Larissa Christina Schudlo

Yusuke Takeuchi

Kourosh Jafari-Khouzani

Frederic Commandeur

Vicky Guo

Arun Krishnan

Aly Mohamed

A mechanism is provided in a data processing system for automatic determination of b-value difference from diffusion-weighted (DW) images. The mechanism receives a series of images wherein a first image has a first b-value and a second image has an unknown b-value. The mechanism applies a generative adversarial network (GAN) model to estimate a difference between b-values in the series of images. The mechanism determines a b-value for the second image based on the first b-value and the estimated difference between b-values.

Automatic determination of b-values from diffusion-weighted magnetic resonance images

An approach for a computer program to receive image data of a subject including at least a portion of a spine of the subject and a chronological age of the subject. The approach includes the computer program pre-processing the image data including at least a portion of a spine. The approach includes determining an apparent age of the spine or a portion of the spine of the subject using a trained artificial intelligence deep learning algorithm.

Estimating spinal age

Disclosed are techniques for automated analysis and assessments of contrast medium absorption phases in contrast medium based medical images. A target image set includes a plurality of medical images acquired to image a plurality of contrast medium absorption phases. For the images of the target image set, a set of contrast medium absorption phase probabilities are determined corresponding to likelihoods that a given image corresponds to a given contrast medium absorption phase. The determined sets of contrast medium absorption phases are compared against a reference set of contrast medium absorption phases to determine differences to determine a set of matching scores indicative of how closely the contrast medium absorption phases of the target image set align with the plurality of contrast medium absorption phases as compared to the reference set of contrast medium absorption phases.

Automated population based assessment of contrast absorption phases

A computer implemented method, a data processing system and a computer program product to determine a likelihood of pneumothorax of a patient, the method including assessing a digital image of a chest x-ray of the patient, applying a standard detection pipeline to the digital image, applying a confounding factor detector to the digital image, and applying a high-resolution detection pipeline to the digital image.

Pneumothorax detection

A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML model(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

Lesion detection artificial intelligence pipeline computing system

A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML modal(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

A method, system, and computer program product provide disease simulation in synthetic projection imagery. The method obtains first medical imaging data of a first imaging type as source imaging data. A second imaging type to be generated from the source imaging data is identified. The method identifies a parameter set for the second imaging type. Second medical imaging data is modeled from the first medical imaging data based on the parameter set. A set of synthetic images is generated from the first medical imaging data based on the modeled second medical imaging data.

Disease simulation in medical images

Techniques for fracture detection are provided. A first image is received to be processed to identify rib fractures. A first set of regions of interest (ROIs) is identified by processing the first image using a first machine learning model, where each ROI in the first set of ROIs corresponds to a first potential fracture. Further, a first ROI of the first set of ROIs is upsampled, and the system attempts to verify the first potential fracture in the first ROI by processing the upsampled first ROI using a second machine learning model.

Automated fracture detection using machine learning models

A computer-implemented method for classifying and presenting a contrast phase (CP) of a contrast enhanced computerized tomography (CECT) scan is provided. The method includes training an artificial intelligence (AI) algorithm utilizing a set of CPs labeled CECT data to associate a set of characteristics of the data with a probability associated with the CP. The method includes receiving a new set of unlabeled CECT data, and applying the AI algorithm to the new unlabeled CECT data to associate a first probability of a first CP and a second probability of a second CP. The method also includes providing a graphical representation including the first probability of the first CP and the second probability of the second CP.

Method of determining contrast phase of a computerized tomography image

Methods and systems for detecting a dissection in surface of an elongated structure in a three dimensional medical image. One system includes an electronic processor configured to receive the three dimensional medical image and determine a periphery of the elongated structure included in the three dimensional medical image. The electronic processor is also configured to generate a non-contrast image representing the periphery of the elongated structure and superimpose a contrast image associated with the three dimensional image on top of the non-contrast image to generate a superimposed image. The electronic processor is also configured to detect at least one dissection in the elongated structure using the superimposed image and output a medical report identifying the at least one dissection detected in the elongated structure.

Vascular dissection detection and visualization using a superimposed image

Methods and systems for detecting a dissection of an elongated structure in a three dimensional medical image. One system includes an electronic processor configured to receive the medical image and detect a centerline of the elongated structure. The electronic processor is configured to determine a plurality of two dimensional cross sections of the medical image based on the centerline. For each of the two dimensional cross sections, the electronic processor is configured to determine a radial density profile and determine a density gradient based on the radial density profile. The electronic processor is configured to analyze one or more of a plurality of density gradients determined for each of the two dimensional cross sections, detect a dissection in the elongated structure based on the analysis of the density gradient for each of the two dimensional cross sections, and output a medical report identifying the dissection.

Vascular dissection detection and visualization using a density profile

Methods and systems for detecting a dissection of an elongated structure in a three dimensional medical image. One system includes an electronic processor that receives the three dimensional medical image. The electronic processor determines a first periphery and a second periphery of the elongated structure, the first periphery and the second periphery associated with an enhancing part and a non-enhancing part, respectively, of the elongated structure. The electronic processor determines whether the first periphery or the second periphery best illustrates an outermost periphery of the elongated structure and generate a base image based on whether the first periphery or the second periphery best illustrates an outermost periphery of the elongated structure. The electronic processor superimposes a contrast image associated with the three dimensional image on top of the base image, detects a dissection in the elongated structure using the superimposed image, and outputs a medical report identifying the dissection.

Vascular dissection detection and visualization using a superimposed image with an improved illustration of an outermost vessel periphery

Systems and methods for determining an abnormality in an elongated structure in a three dimensional medical image. One system includes an electronic processor. The electronic processor is configured to determine a centerline of the elongated structure in the three dimensional medical image and determine a plurality of two dimensional cross sections of the three dimensional medical image based on the centerline. For each two dimensional cross section of the plurality of two dimensional cross sections, the electronic processor is configured to convert the two dimensional cross section to polar coordinates, fit a line to the elongated structure in the two dimensional cross section converted to polar coordinates, and reconvert the two dimensional cross section to Cartesian coordinates.

Determining centerlines in elongated structures in images to detect abnormalities

A mechanism is provided to implement an artificial intelligence (AI) based alert mechanism system for alerting a medical professional of potential inaccuracies in medical image analysis. Responsive to receiving a medical image of a patient and a radiology report associated with the medical image, the AI based alert mechanism analyzes the radiology report to identify medical findings detected by the medical professional and analyzes the medical image to detect one or more medical findings associated with the medical image. Responsive to the AI based alert mechanism identifying one or more medical findings, the AI based alert mechanism compares the identified medical findings to those medical findings identified in the radiology report. Responsive to the AI based alert mechanism identifying a discrepancy between the identified medical findings to those in the radiology report, the AI based alert mechanism generates an alert to the medical professional who generated the radiology report.

Artificial intelligence based alert system

Systems and methods for generating a training example to train artificial intelligence software to automatically determine a centerline of an elongated structure of three dimensional images. One system includes an electronic processor configured to receive a plurality of reference points for a subset of a plurality of slices of a first three dimensional image. Each of the plurality of reference points marks a centerline of the elongated structure within one of the subset of the plurality of slices. The electronic processor is configured to determine an order of the plurality of reference points and fit a spline curve to the plurality of reference points based on the order of the reference points to create the training example. The electronic processor is further configured use the training example to train the artificial intelligence software to automatically determine a centerline of an elongated structure in a second three dimensional medical image.

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Ashland, MA, United States of America

Arkadiusz Sitek