Convolutional neural network for identification of anatomical landmark

An apparatus, comprising:

• a processor communicatively coupled to an ultrasound imaging device,
• wherein the processor is configured to:
• obtain an ultrasound image of an anatomical area from an ultrasound device;
• input the ultrasound image into a first stage of a convolutional neural network, the first stage configured to determine a key-point location of the anatomical area;
• generate, for the key-point location, a cropped region of the ultrasound image;
• input the cropped region into a second stage of the convolutional neural network, the second stage configured to locate an anatomical landmark of the anatomical area;
• and
• output a location of the anatomical landmark.

A system, comprising:

• an ultrasound imaging device configured to capture and ultrasound image;
• and
• a computing device communicatively coupled to the ultrasound imaging device,
• wherein the computing device is configured to:
• obtain an ultrasound image of an anatomical area from the ultrasound imaging device;
• input the ultrasound image into a first stage of a convolutional neural network, the first stage configured to determine a key-point location of the anatomical area;
• generate, the key-point location, a cropped region of the ultrasound image;
• input the cropped region into a second stage of the convolutional neural network, the second stage configured to locate an anatomical landmark of the anatomical area;
• and
• output a location of the anatomical landmark.

A non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by a processor, cause the processor to:

• obtain an ultrasound image of an anatomical area from an ultrasound imaging device;
• input the ultrasound image into a first stage of a convolutional neural network, the first stage configured to determine a key-point location of the anatomical area;
• generate, for each of the key-point locations, a cropped region of the ultrasound image;
• input the cropped region into a second stage of the convolutional neural network, the second stage configured to locate an anatomical landmark of the anatomical area;
• and
• output a location of the anatomical landmark.