Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Medical Solutions Usa, Inc.

Eli Gibson

Dorin Comaniciu

Bogdan Georgescu

Youngjin Yoo

Thomas Re

Sasa Grbic

Andrei Chekkoury

Pascal Ceccaldi

Zhoubing Xu

Siqi Liu

Arnaud Arindra Adiyoso

Florin-Cristian Ghesu

Jyotipriya Das

Eva Eibenberger

Gengyan Zhao

Thomas Flohr

Mariappan S Nadar

Boris Mailhe

Vivek Kumar Singh

Gianluca Paladini

Guillaume Chabin

Ahmet Tuysuzoglu

Poikavila Ullaskrishnan

Sebastian Guendel

Nguyen Nguyen

Jie Yang

Pragneshkumar Patel

Barbara Brehm

Pierre-Hugo Trigan

Systems and methods for generating augmented images are provided. One or more input medical images are received. At least one of noise and one or more transformations are applied to the one or more input medical images to generate one or more noisy augmented images. The one or more noisy augmented images are denoised using a diffusion-based denoising system to generate one or more denoised augmented images. The applying and the denoising are repeated for one or more iterations using the one or more denoised augmented images as the one or more input medical images to generate one or more final augmented images. The one or more final augmented images are output.

Machine learning data augmentation using diffusion-based generative models

Systems and methods for generating synthesized medical images of a tumor are provided. A 3D mask of an anatomical structure generated from a 3D medical image and a 3D image of a plurality of concentric spheres are received. A 3D mask of a tumor is generated based on the 3D mask of the anatomical structure and the 3D image of the plurality of concentric spheres using a first 3D generator network. A 3D intensity map of the tumor is generated based on the 3D mask of the tumor and the 3D image of the plurality of concentric spheres using a second 3D generator network. A 3D synthesized medical image of the tumor is generated based on one or more 2D slices of the 3D intensity map of the tumor and one or more 2D slices of the 3D medical image using a 2D generator network. The 3D synthesized medical image of the tumor is output.

Synthesis of medical images of brain tumors using 3D-2D GANs

A scheduling system includes: a plurality of input devices configured to output medical data, a workforce storage, configured to store working characteristics of a plurality of doctors, and a scheduler configured to receive as input data related to the medical data and the working characteristics, and configured to provide as output a plurality of schedules for the plurality of doctors for analysing the medical data.

Medical data management system, method and computer program

Systems and methods for occlusion detection in medical images are provided. An input medical image of one or more vessels in an anatomical object of a patient is received. One or more anatomical landmarks are identified in the input medical image. A first patch and one or more additional patches are extracted from the input medical image based on the identified one or more anatomical landmarks. The first patch and the one or more additional patches depict different portions of the anatomical object. Features are extracted from the first patch and the one or more additional patches using a machine learning based feature extractor network. An occlusion in the one or more vessels is detected in the first patch based on the extracted features with or without modeling features on a probability distribution function. Results of the detecting are output.

Large vessel occlusion detection and classification in medical imaging

Systems and methods for determining a segmentation of a hemorrhage are provided. An input medical image of a hemorrhage of a patient is received. A contour-sensitive segmentation of the hemorrhage from the input medical image is performed using a machine learning based contour-sensitive segmentation network. A detection-sensitive segmentation of the hemorrhage from the input medical image is performed using a machine learning based detection-sensitive segmentation network. A final segmentation of the hemorrhage from the input medical image is determined based on results of the contour-sensitive segmentation and results of the detection-sensitive segmentation. The final segmentation of the hemorrhage is output.

Acute intracranial hemorrhage segmentation on brain images for volume quantification and individual detection

Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.

Machine learning for automatic detection of intracranial hemorrhages with uncertainty measures from medical images

Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.

Probabilistic tree tracing and large vessel occlusion detection in medical imaging

Systems and methods facilitate privacy-preserving data curation in a federated learning system by transmitting a portion of a potential data sample to a remote location. The portion is inspected for quality to rule out data samples that do not satisfy data curation criteria. The remote examination focuses on checking the region of interest but maintains privacy as the examination is unable to parse any other identifiable subject information such as face, body shape etc. because pixels or voxels outside the portion are not included. The examination results are sent back to the collaborators so that inappropriate data samples can be excluded during federated learning rounds.

Privacy-preserving data curation for federated learning

Systems and methods for quantifying a shift of an anatomical object of a patient are provided. A 3D medical image of an anatomical object of a patient is received. An initial location of landmarks on the anatomical object in the 3D medical image is determined using a first machine learning network. A 2D slice depicting the initial location of the landmarks is extracted from the 3D medical image. The initial location of the landmarks in the 2D slice is refined using a second machine learning network. A shift of the anatomical object is quantified based on the refined location of the landmarks in the 2D slice. The quantified shift of the anatomical object is output.

Automated estimation of midline shift in brain ct images

Systems and methods for assessing expansion of an abnormality are provided. A first input medical image of a patient depicting an abnormality at a first time and a second input medical image of the patient depicting the abnormality at a second time are received. The second input medical image is registered with the first input medical image. The abnormality is segmented from 1) the first input medical image to generate a first segmentation map and 2) the registered second input medical image to generate a second segmentation map. The first segmentation map and the second segmentation map are combined to generate a combined map. Features are extracted from the first input medical image and the registered second input medical image are based on the combined map. Expansion of the abnormality is assessed based on the extracted features using a trained machine learning based network. Results of the assessment are output.

Automatic hemorrhage expansion detection from head CT images

Systems and methods for automatic segmentation of lesions from a 3D input medical image are provided. A 3D input medical image depicting one or more lesions is received. The one or more lesions are segmented from one or more 2D slices extracted from the 3D input medical image using a trained 2D segmentation network. 2D features are extracted from results of the segmentation of the one or more lesions from the one or more 2D slices. The one or more lesions are segmented from a 3D patch extracted from the 3D input medical image using a trained 3D segmentation network. 3D features are extracted from results of the segmentation of the one or more lesions from the 3D patch. The extracted 2D features and the extracted 3D features are fused to generate final segmentation results. The final segmentation results are output.

Automatic detection of lesions in medical images using 2D and 3D deep learning networks

Medical images may be classified by receiving a first medical image. The medical image may be applied to a machine learned classifier. The machine learned classifier may be trained on second medical images. A label of the medical image and a measure of uncertainty may be generated. The measure of uncertainty may be compared to a threshold. The first medical image and the label may be output when the measure of uncertainty is within the threshold.

Medical image assessment with classification uncertainty

For saliency mapping, a machine-learned classifier is used to classify input data. A perturbation encoder is trained and/or applied for saliency mapping of the machine-learned classifier. The training and/or application (testing) of the perturbation encoder uses less than all feature maps of the machine-learned classifier, such as selecting different feature maps of different hidden layers in a multiscale approach. The subset used is selected based on gradients from back-projection. The training of the perturbation encoder may be unsupervised, such as using an entropy score, or semi-supervised, such as using the entropy score and a difference of a perturbation mask from a ground truth segmentation.

Saliency mapping by feature reduction and perturbation modeling in medical imaging

Systems and methods are provided for generating a synthesized medical image patch of a nodule. An initial medical image patch and a class label associated with a nodule to be synthesized are received. The initial medical image patch has a masked portion and an unmasked portion. A synthesized medical image patch is generated using a trained generative adversarial network. The synthesized medical image patch includes the unmasked portion of the initial medical image patch and a synthesized nodule replacing the masked portion of the initial medical image patch. The synthesized nodule is synthesized according to the class label. The synthesized medical image patch is output.

Class-aware adversarial pulmonary nodule synthesis

Systems and methods for generating synthesized images are provided. An input medical image patch, a segmentation mask, a vector of appearance related parameters, and manipulable properties are received. A synthesized medical image patch including a synthesized nodule is generated based on the input medical image patch, the segmentation mask, the vector of appearance related parameters, and the manipulable properties using a trained object synthesis network. The synthesized nodule is synthesized according to the manipulable properties. The synthesized medical image patch is output.

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Plainsboro, NJ, United States of America

Eli Gibson