Abstract: Post-scan ultrasound images are masked and labeled with anatomical features identified in the images. Masks corresponding to the features are transformed into the coordinate system of the pre-scan, raw ultrasound data frames corresponding to the images. The masks and their respective raw ultrasound data frames are used to train an artificial intelligence model. The model is then used to predict the presence of anatomical features of the same type in other pre-scan ultrasound data. Once predicted, the raw data is scan converted for display as segmented ultrasound images that identify the predicted anatomical features. Deployment of the model on pre-scan data avoids the need to account for different levels of processing that may be applied to post-scan images, while the labeling for training the model may be done on the post-scan images.

Abstract: Post-scan ultrasound images are masked and labeled with anatomical features identified in the images. Masks corresponding to the features are transformed into the coordinate system of the pre-scan, raw ultrasound data frames corresponding to the images. The masks and their respective raw ultrasound data frames are used to train an artificial intelligence model. The model is then used to predict the presence of anatomical features of the same type in other pre-scan ultrasound data. Once predicted, the raw data is scan converted for display as segmented ultrasound images that identify the predicted anatomical features. Deployment of the model on pre-scan data avoids the need to account for different levels of processing that may be applied to post-scan images, while the labeling for training the model may be done on the post-scan images.

Abstract: A method of processing hyperspectral data includes receiving the hyperspectral data. The hyperspectral data includes spectral data for each pixel in a two-dimensional array of pixels, and for each spectral band in a set of multiple spectral bands associated with each pixel. The hyperspectral data is converted into one-dimensional spectra. Each one-dimensional spectrum includes, for a single pixel of the pixels, the spectral data for each spectral band in the set of multiple spectral bands associated with the single pixel. Each one-dimensional spectrum is inputted to a trained transformer neural network. For each one-dimensional spectrum, the trained transformer neural network is used to spectrally un-mix the spectral data in the set of multiple spectral bands.

Abstract: Hyperspectral imaging is used to identify one or more materials in an object. The object is illuminated with light. At least some of the light is reflected by the object. A hyperspectral imaging sensor captures, based on the reflected light, one or more hyperspectral images of the object, the one or more hyperspectral images include hyperspectral data. The one or more hyperspectral images are input to a trained machine learning model. The trained machine learning model spectrally un-mixes the hyperspectral data so as to extract one or more spectral signatures from the hyperspectral data. Based on the one or more extracted spectral signatures, one or more materials comprised in the object are extracted. Another trained machine learning model is used to detect the shape of the object.

Abstract: A plurality of ultrasound frames of a fetus are acquired using an ultrasound scanner, which may be oriented arbitrarily with respect to the fetus during the acquisition. The ultrasound frames are processed against an artificial intelligence model to predict a different cut line on each of the ultrasound frames. Each cut line is predicted to be exterior to an image of the fetus appearing on the ultrasound frame. The different cut lines on the plurality of ultrasound frames are then used to identify ultrasound data in the image frames to generate a 3D representation of the fetus.

Abstract: A multi-media product is created from fetal ultrasound images, during scanning of a fetus using an ultrasound scanner, and employs a specifically trained artificial intelligence (AI) model to execute on a computing device communicably connected to an ultrasound scanner, wherein the AI model is trained so that when the AI model is deployed, the computing device identifies and selects one or more fetal anatomical features, in whole or part, imaged in fetal ultrasound imaging data generated during ultrasound scanning as part of a clinical exam of the fetus, wherein the selected one or more fetal anatomical features are visually appealing for entertainment and keepsake purposes and are not part of a clinical assessment of the health or growth of the fetus and wherein after acquiring a new fetal ultrasound image during ultrasound scanning, the AI model selects the one or more fetal anatomical features, in whole or part, which are visually appealing for entertainment and keepsake purposes and are not part of a clin

Abstract: A plurality of ultrasound frames of a fetus are acquired using an ultrasound scanner, which may be oriented arbitrarily with respect to the fetus during the acquisition. The ultrasound frames are processed against an artificial intelligence model to predict a different cut line on each of the ultrasound frames. Each cut line is predicted to be exterior to an image of the fetus appearing on the ultrasound frame. The different cut lines on the plurality of ultrasound frames are then used to identify ultrasound data in the image frames to generate a 3D representation of the fetus.

Abstract: A method and system provide for the identification of a tendon in ultrasound imaging data, by deploying an artificial intelligence (AI) model to execute on a computing device, wherein the AI model is trained to identify a plurality of different types of tendons imaged in ultrasound imaging data, and when deployed, the computing device generates a probability for each of the plurality of different types of tendons that the type of tendon is imaged in new ultrasound imaging data and wherein such probability is corroborated by at least one live deployment input.

Abstract: A plurality of ultrasound frames of a fetus are acquired using an ultrasound scanner, which may be oriented arbitrarily with respect to the fetus during the acquisition. The ultrasound frames are processed against an artificial intelligence model to predict a different cut line on each of the ultrasound frames. Each cut line is predicted to be exterior to an image of the fetus appearing on the ultrasound frame. The different cut lines on the plurality of ultrasound frames are then used to identify ultrasound data in the image frames to generate a 3D representation of the fetus.

Abstract: Post-scan ultrasound images are masked and labeled with anatomical features identified in the images. Masks corresponding to the features are transformed into the coordinate system of the pre-scan, raw ultrasound data frames corresponding to the images. The masks and their respective raw ultrasound data frames are used to train an artificial intelligence model. The model is then used to predict the presence of anatomical features of the same type in other pre-scan ultrasound data. Once predicted, the raw data is scan converted for display as segmented ultrasound images that identify the predicted anatomical features. Deployment of the model on pre-scan data avoids the need to account for different levels of processing that may be applied to post-scan images, while the labeling for training the model may be done on the post-scan images.

Abstract: A plurality of ultrasound frames of a fetus are acquired using an ultrasound scanner, which may be oriented arbitrarily with respect to the fetus during the acquisition. The ultrasound frames are processed against an artificial intelligence model to predict a different cut line on each of the ultrasound frames. Each cut line is predicted to be exterior to an image of the fetus appearing on the ultrasound frame. The different cut lines on the plurality of ultrasound frames are then used to identify ultrasound data in the image frames to generate a 3D representation of the fetus.