Noise reduction in ultrasound images

A method implemented by a computing device, the method comprising:

• generating a first ultrasound image based on ultrasound data, the ultrasound data representing reflections of ultrasound signals transmitted by an ultrasound scanner at an anatomy;
• generating, based on the first ultrasound image and by a noise-reduction system implemented at least partially in hardware of the computing device, a second ultrasound image that has less noise than the first ultrasound image, wherein generating the second ultrasound image includes:
• decomposing the first ultrasound image into multiple scale images having different scales;
• wherein:
• the multiple scale images include at least a first scale image and a second scale image;
• the first scale image has a first scale;
• the second scale image has a second scale that is different from the first scale;
• and
• the second scale image represents a different region of interest than that of the first scale image;
• selecting a first machine-learned model to process the first scale image;
• and
• selecting a second machine-learned model to process the second scale image;
• determining, based on the second ultrasound image generated, state information of the noise-reduction system, the state information representing an internal state of the noise-reduction system;
• and
• generating, with a third machine-learned model implemented at least partially in the hardware of the computing device and based on the second ultrasound image and the state information, at least one of an identification, a segmentation, or a classification of one or more objects including the anatomy.

An ultrasound system comprising:

• an ultrasound scanner configured to generate ultrasound data, the ultrasound data based on reflections of ultrasound signals transmitted by the ultrasound scanner at an anatomy;
• one or more processors configured to generate a first ultrasound image based on the ultrasound data;
• a noise-reduction system configured to:
• preprocess the first ultrasound image;
• generate a preprocessed ultrasound image having reduced noise relative to the first ultrasound image;
• and
• determine state information of the noise-reduction system based on the preprocessed ultrasound image generated, the state information including parameters and settings used by the noise-reduction system to generate the preprocessed ultrasound image from the first ultrasound image;
• and
• one or more computer-readable media having instructions stored thereon that, responsive to execution by the one or more processors, implement one or more modules, the one or more modules configured to:
• provide the preprocessed ultrasound image as input and the state information as secondary input to a machine-learned model implemented at least partially in hardware of the ultrasound system;
• and
• obtain an output from the machine-learned model, the output including at least one of an identification, a segmentation, or a classification of an object represented in the preprocessed ultrasound image.