Prediction method for healthy radius of blood vessel path, prediction method for candidate stenosis of blood vessel path, and blood vessel stenosis degree prediction device

A blood vessel stenosis degree prediction system, comprising:

• an interface configured to receive an image of a blood vessel;
• a processor configured to:
• extract a blood vessel path and its centerline based on the image of the blood vessel;
• determine a candidate stenosis for the blood vessel path;
• identify image blocks along the centerline of the blood vessel path within a range of candidate stenosis for the blood vessel path determined based on the candidate stenosis;
• and
• determine a degree of stenosis for the blood vessel path by applying a trained learning network comprising a convolutional neural network and a recurrent neural network on the image blocks within the range of candidate stenosis,
• wherein the convolutional neural network and the recurrent neural network are sequentially applied, wherein the convolutional neural network is applied on the image blocks along the centerline of the blood vessel path to generate vectors for the respective image blocks, wherein the recurrent neural network is applied on the vectors.

A non-transitory storage medium having stored thereon computer executable instructions that, when executed by a processor, implement a method for predicting a blood vessel stenosis, the method comprising:

• extracting a blood vessel path and its centerline based on an image of a blood vessel;
• determining a candidate stenosis for the blood vessel path;
• identifying image blocks along the centerline of the blood vessel path within a range of candidate stenosis for the blood vessel path determined based on the candidate stenosis;
• and
• determining a degree of stenosis for the blood vessel path by applying a trained learning network comprising a convolutional neural network and a recurrent neural network on the image blocks within the range of candidate stenosis,
• wherein the convolutional neural network and the recurrent neural network are sequentially applied, wherein the convolutional neural network is applied on the image blocks along the centerline of the blood vessel path to generate vectors for the respective image blocks, wherein the recurrent neural network is applied on the vectors.