Ultrasound image feature segmentation
By example, a method for training a model to segment test images, wherein the test images comprise ultrasound image data, includes: receiving, at a self-supervised learning framework, a first plurality of training images, wherein the first plurality of training images include ultrasound data corresponding to patients' livers; processing the first plurality of plurality of training images with a learning algorithm of the self-supervised learning framework, and responsively adapting a trained model; and receiving, at a supervised learning framework, the trained model and a second plurality of training images, wherein the second plurality of training images include ultrasound data corresponding to patients' livers and annotations of the livers, and responsively adapting the trained model.
1 . A method for segmenting structures in ultrasound image data, the method comprising:
obtaining, using ultrasonic energy, ultrasound image data of a patient, including a liver;
receiving, at a processor, the ultrasound image data;
executing, by the processor, inference instructions to inference from a trained artificial intelligence model to segment, in the ultrasound image data, the liver in real-time to form a segmented liver, and to segment a poor-probe-contact region of an ultrasound probe with the patient's skin; and
presenting, on a display, the ultrasound image data and information corresponding to the poor-probe-contact region with the ultrasound image data.
2 . The method of claim 1 , further comprising:
determining a region of interest within the segmented liver; and
performing shear-wave elastography on data obtained from the region of interest.
3 . A system, comprising:
an ultrasound probe and receiver configured to obtain ultrasound image data of a patient, including a liver;
a processor configured to receive the ultrasound image data and to execute inference instructions to inference from a trained artificial intelligence model to segment, in the ultrasound image data, the liver in real-time to form a segmented liver and to segment, in the ultrasound image data, a poor probe contact region of an ultrasound probe with the patient's skin; and
a display configured to present the ultrasound image data and information associated with the segmented liver and further configured to present information corresponding to the poor-probe-contact region with the ultrasound image data.
4 . The system of claim 3 , wherein the processor is further configured to determine a region of interest within the segmented liver, and cause a shear-wave elastography process to be performed to obtain shear-wave elastography data from the region of interest.
5 . The method of claim 1 , wherein the trained artificial intelligence model is adaptively trained by a process comprising:
receiving, at a self-supervised learning framework, a first plurality of training images, wherein the first plurality of training images include ultrasound data corresponding to patients' livers;
processing the first plurality of training images with a learning algorithm of the self-supervised learning framework, and responsively adapting the trained artificial intelligence model; and
receiving, at a supervised learning framework, the trained artificial intelligence model and a second plurality of training images, wherein the second plurality of training images include ultrasound data corresponding to patients' livers and annotations of the patients' livers, and responsively adapting the trained artificial intelligence model.
6 . The method of claim 5 , wherein the self-supervised learning framework comprises a contrastive learning framework.
7 . The method of claim 6 , wherein the self-supervised learning framework employs a convolutional neural network as an encoder.
8 . The method of claim 6 , wherein the self-supervised learning framework employs a projection head.
9 . The method of claim 5 , wherein the self-supervised learning framework comprises a SimCLR framework.
10 . The method of claim 5 , wherein the supervised learning framework comprises an ENet framework.
11 . The method of claim 5 , wherein the supervised learning framework comprises an encoder including a plurality of stages and a decoder including a plurality of stages, wherein each stage includes a plurality of bottleneck modules configured to manage dimensionality.
12 . The method of claim 5 , wherein the supervised learning framework comprises a maximum pooling layer, wherein the self-supervised framework further comprises a decoder including a maximum unpooling layer and a spatial convolution algorithm.
13 . The method of claim 5 , wherein the supervised learning framework is configured to avoid bias terms in projections.
14 . A system, comprising:
an ultrasound probe and receiver configured to obtain ultrasound image data of a patient, including a liver;
a processor configured to receive the ultrasound image data and to execute inference instructions to inference from a trained artificial intelligence model to segment, in the ultrasound image data, the liver in real-time to form a segmented liver;
a display configured to present the ultrasound image data and information associated with the segmented liver; and
wherein the trained artificial intelligence model is trained by a process comprising:
receiving, at a self-supervised learning framework, a first plurality of training images, wherein the first plurality of training images include ultrasound data corresponding to patients' livers;
processing the first plurality of training images with a learning algorithm of the self-supervised learning framework, and responsively adapt the trained artificial intelligence model; and
receiving, at a supervised learning framework, the trained artificial intelligence model and a second plurality of training images, wherein the second plurality of training images include ultrasound data corresponding to patients' livers and annotations of the livers, and adapt adapting the trained artificial intelligence model,
wherein the processor is further configured to execute inference instructions to segment, in the ultrasound image data, a poor-probe-contact region of an ultrasound probe with a patient's skin, and
wherein the display is further configured to present information corresponding to the poor-probe-contact region with the ultrasound image data.
15 . The system of claim 14 , wherein the processor is further configured to determine a region of interest within the segmented liver, and cause a shear-wave elastography process to be performed to obtain shear-wave elastography data from the region of interest.