Abstract: Method for detecting spatial coupling comprising the steps of: a. providing a set of data, b. identifying and segmenting a first and a second sets of objects of interest, wherein the objects of the second set are assimilated to punctual objects, c. determining, using a level set function, an expected number of objects of the second set present within a specified range of distances to at least one given object of the first set in case there were no interactions between said at least one given object of the first set and the objects of the second set, d. determining, using a level set function, an actual number of objects of the second set within the same range of distances to the at least one given object of the first set, and e. comparing said expected amount and said determined amount.

Abstract: A system (e.g., an ultrasound imaging system) is provided. The system includes an ultrasound probe configured to acquire three-dimensional (3D) ultrasound data of a volumetric region of interest (ROI). The system further includes a display, a memory configured to store programmed instructions, and a controller circuit. The controller circuit includes one or more processors. The controller circuit is configured to execute the programmed instructions stored in the memory. When executing the programmed instructions, the controller circuit performs a plurality of operations. The operations includes collecting the 3D ultrasound data from an ultrasound probe and identifying a select set of the 3D ultrasound data corresponding to an object of interest within the volumetric ROI. The operations further include segmenting the object of interest from the select set of the 3D ultrasound data, generating a visualization plane of the object of interest, and displaying the visualization plane on the display.

Abstract: A method for determining a nervous system condition includes obtaining an estimate of a first scan plane among a plurality of planes of a maternal subject using a first deep learning network during a guided scanning procedure. The method further includes receiving a three-dimensional (3D) ultrasound volume corresponding to the initial estimate and determining an optimal first scan plane from the first deep learning network. The method further includes determining at least one of a second scan plane, a third scan plane and a fourth scan plane among the plurality of planes, based on the optimal first scan plane and at least one of a clinical constraint corresponding to the plurality of planes using a second deep learning network. The method includes determining a biometric parameter corresponding to nervous system based on at least one of the plurality of planes using a third deep learning network.

Abstract: A system (e.g., an ultrasound imaging system) is provided. The system includes an ultrasound probe configured to acquire three-dimensional (3D) ultrasound data of a volumetric region of interest (ROI). The system further includes a display, a memory configured to store programmed instructions, and a controller circuit. The controller circuit includes one or more processors. The controller circuit is configured to execute the programmed instructions stored in the memory. When executing the programmed instructions, the controller circuit performs a plurality of operations. The operations includes collecting the 3D ultrasound data from an ultrasound probe and identifying a select set of the 3D ultrasound data corresponding to an object of interest within the volumetric ROI. The operations further include segmenting the object of interest from the select set of the 3D ultrasound data, generating a visualization plane of the object of interest, and displaying the visualization plane on the display.

Abstract: Embodiments described herein provide a hybrid technique which incorporates learned pulmonary nodule features in a model based energy minimization segmentation using graph cuts. Features are extracted from training samples using a convolutional neural network, and the segmentation cost function is augmented via the deep learned energy. The system and method improves segmentation performance and more robust initialization.

Abstract: Embodiments described herein provide a hybrid technique which incorporates learned pulmonary nodule features in a model based energy minimization segmentation using graph cuts. Features are extracted from training samples using a convolutional neural network, and the segmentation cost function is augmented via the deep learned energy. The system and method improves segmentation performance and more robust initialization.

Abstract: A system (e.g., an ultrasound imaging system) is provided. The system includes an ultrasound probe configured to acquire three dimensional (3D) ultrasound data of a volumetric region of interest (ROI). The system further includes a display, a memory configured to store programmed instructions, and a controller circuit. The controller circuit includes one or more processors. The controller circuit is configured to execute the programmed instructions stored in the memory. When executing the programmed instructions, the controller circuit performs a plurality of operations. The operations includes collecting the 3D ultrasound data from an ultrasound probe and identifying a select set of the 3D ultrasound data corresponding to an object of interest within the volumetric ROI. The operations further include segmenting the object of interest from the select set of the 3D ultrasound data, generating a visualization plane of the object of interest, and displaying the visualization plane on the display.