Workflow for automatic measurement of doppler pipeline

A computer program product for generating feature vector representations of Doppler echocardiography frames, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:

• reading a plurality of frames of a medical video, wherein each frame of the medical video comprises a Doppler pattern;
• providing at least one of the plurality of frames to a trained feature generator comprising a convolutional neural network, the convolutional neural network trained using images from an imaging domain other than Doppler echocardiography, the convolutional neural network comprising a plurality of layers, the plurality of layers comprising a first fully-connected layer and a output layer comprising a plurality of output nodes;
• obtaining from the trained feature generator at least one feature vector corresponding to the at least one of the plurality of frames, wherein the at least one feature vector is obtained from the first fully-connected layer of the convolutional neural network, the at least one feature vector representing features applicable in Doppler echocardiography and the imaging domain other than Doppler echocardiography;
• determining an ultrasound mode label indicative of an ultrasound mode for each of the plurality of frames;
• selecting one of a plurality of valve detection stages according to the ultrasound mode label, the selected valve detection stage comprising a trained classifier;
• providing the at least one feature vector to the trained classifier;
• obtaining from the trained classifier a valve label indicative of a valve represented in the at least one of the plurality of frames;
• based on the obtained valve label, determining whether aortic stenosis is present;
• and
• based on the determination, treating the aortic stenosis with one or more transcatheter aortic valve replacements.

A system comprising:

• a medical imaging records database;
• a computing node comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:
• reading a plurality of frames of a medical video from the medical imaging records database, wherein each frame of the medical video comprises a Doppler pattern;
• providing at least one of the plurality of frames to a trained feature generator comprising a convolutional neural network, the convolutional neural network trained using images from an imaging domain other than Doppler echocardiography, the convolutional neural network comprising a plurality of layers, the plurality of layers comprising a first fully-connected layer and a output layer comprising a plurality of output nodes;
• obtaining from the trained feature generator at least one feature vector corresponding to the at least one of the plurality of frames, wherein the at least one feature vector is obtained from the first fully-connected layer of the convolutional neural network, the at least one feature vector representing features applicable in Doppler echocardiography and the imaging domain other than Doppler echocardiography;
• determining an ultrasound mode label indicative of an ultrasound mode for each of the plurality of frames;
• selecting one of a plurality of valve detection stages according to the ultrasound mode label, the selected valve detection stage comprising a trained classifier;
• providing the at least one feature vector to the trained classifier;
• obtaining from the trained classifier a valve label indicative of a valve represented in the at least one of the plurality of frames;
• based on the obtained valve label, determining whether aortic stenosis is present;
• and
• based on the determination, treating the aortic stenosis with one or more transcatheter aortic valve replacements.