Abstract: Systems and methods for automatically segmenting and detecting a menstrual cycle phase in ultrasound images of anatomical structures that change over a patient menstrual cycle are provided. The method includes acquiring, by an ultrasound probe of an ultrasound system, an ultrasound image of a region of interest having an anatomical structure that changes over a patient menstrual cycle. The method includes automatically segmenting, by at least one processor of the ultrasound system, an anatomical structure depicted in the ultrasound image. The method includes automatically predicting, by the at least one processor, a menstrual cycle phase based on the segmentation of the anatomical structure. The method includes causing, by the at least one processor, a display system to present at least one rendering of the segmented anatomical structure and the predicted menstrual cycle phase.

Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.

Abstract: The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.

Abstract: Systems/techniques that facilitate two-tiered machine learning generation of birth risk score are provided. In various embodiments, a system can access a plurality of medical feature collections associated with a pregnant patient. In various aspects, the system can generate, via execution of a plurality of first trained machine learning models, a plurality of embedded features based on the plurality of medical feature collections. In various instances, the system can compute, via execution of a second trained machine learning model, a risk score based on the plurality of embedded features, wherein the risk score indicates an amount of risk to a health of the pregnant patient or a health of a fetus of the pregnant patient that is associated with performing a caesarian-section on the pregnant patient or with waiting for the pregnant patient to give birth naturally.

Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.

Abstract: Systems and methods for workflow management for labeling the subject anatomy are provided. The method comprises obtaining at least one localizer image of a subject anatomy using a low-resolution medical imaging device. The method further comprises labeling at least one anatomical point within the at least one localizer image. The method further comprises extracting using a machine learning module a mask of the at least one localizer image comprising the at least one anatomical point label. The method further comprises using the mask to label at least one anatomical point on a high-resolution image of the subject anatomy based on the at least one anatomical point within the localizer image.

Abstract: Systems and methods are provided for projection profile enabled computer aided detection (CAD). Volumetric ultrasound dataset may be generated, based on echo ultrasound signals, and based on the volumetric ultrasound dataset, a three-dimensional (3D) ultrasound volume may generated. Selective structure detection may be applied to the three-dimensional (3D) ultrasound volume.

Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.

Abstract: The present disclosure relates to a system and method for identifying a tumor or lesion in a probability map. In accordance with certain embodiments, a method includes identifying, with a processor, a first region of interest in a first projection image, generating, with the processor, a first probability map from the first projection image and a second probability map from a second projection image, wherein the first probability map includes a second region of interest that has location that corresponds to a location of the first region of interest, interpolating the first probability map and the second probability map, thereby generating a probability volume, wherein the probability volume includes the second region of interest, and outputting, with the processor, a representation of the probability volume to a display.

Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

Abstract: A method for assisted reading of automated ultrasound image volumes includes receiving a plurality of scan images generated from an imaging device, wherein the plurality of scan images comprises a chest wall region. The method further includes determining a chest wall model representative of the chest wall region based on the plurality of scan images. The method also includes determining a plurality of segmented scan images segmented along the chest wall region based on the chest wall model. In addition, the method includes determining lesion information using an automated lesion detection technique applied to the plurality of segmented scan images. The method also includes displaying the plurality of scan images along with at least one of the lesion information and the chest wall model.

Abstract: Systems and methods are provided for projection profile enabled computer aided detection (CAD). Volumetric ultrasound dataset may be generated, based on echo ultrasound signals, and based on the volumetric ultrasound dataset, a three-dimensional (3D) ultrasound volume may generated. Selective structure detection may be applied to the three-dimensional (3D) ultrasound volume.

Abstract: The present disclosure provides, in certain implementations, a rule-based or deep learning-based approach capable of assessing diagnostic utility of images in near real time with respect to acquisition. Correspondingly, an automated implementation of such an algorithm on the scanner would, in fact, emulate the doctor himself rating images in real time, and reduce the number of unneeded re-scans and recalls. In one aspect of the present invention it was found that diagnostic utility of an image is not an absolute measure, but instead depends upon the reading radiologist and the scan indication (i.e., the purpose of the scan). Therefore, adapting the threshold (probability of an imaging volume to be deemed good) as a function of reading radiologist and scan indication can result in decreasing the number of re-scans and recalls.

Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.

Abstract: The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.

Abstract: The present disclosure provides, in certain implementations, a rule-based or deep learning-based approach capable of assessing diagnostic utility of images in near real time with respect to acquisition. Correspondingly, an automated implementation of such an algorithm on the scanner would, in fact, emulate the doctor himself rating images in real time, and reduce the number of unneeded re-scans and recalls. In one aspect of the present invention it was found that diagnostic utility of an image is not an absolute measure, but instead depends upon the reading radiologist and the scan indication (i.e., the purpose of the scan). Therefore, adapting the threshold (probability of an imaging volume to be deemed good) as a function of reading radiologist and scan indication can result in decreasing the number of re-scans and recalls.

Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.

Abstract: A method for assisted reading of automated ultrasound image volumes includes receiving a plurality of scan images generated from an imaging device, wherein the plurality of scan images comprises a chest wall region. The method further includes determining a chest wall model representative of the chest wall region based on the plurality of scan images. The method also includes determining a plurality of segmented scan images segmented along the chest wall region based on the chest wall model. In addition, the method includes determining lesion information using an automated lesion detection technique applied to the plurality of segmented scan images. The method also includes displaying the plurality of scan images along with at least one of the lesion information and the chest wall model.