Blood vessel segment discrimination system, blood vessel segment discrimination method, and program

A blood vessel segment discrimination method for a blood vessel segment discrimination system including:

• a three-dimensional structure recognition device configured to recognize a three-dimensional structure of an abdomen of the patient;
• a depth image generation unit configured to generate a depth image of the abdomen of the patient from the three-dimensional structure of the abdominal surface of the patient recognized by the three-dimensional structure recognition device;
• a blood vessel segment discrimination device configured to discriminate an aortic segment of the patient whose three-dimensional structure of the abdominal surface is recognized by the three-dimensional structure recognition device, using a deep learning model;
• and
• a training data set generation unit configured to generate a training data set used for training of the deep learning model, the blood vessel segment discrimination method comprising:
• a training data set generation step of, before the training of the deep learning model is performed, via the training data set generation unit,
• generating the three-dimensional structure of the abdominal surface for training from any of an abdominal CT image, an abdominal MRI image, and an abdominal MRA image of a person different from a patient who is a discrimination target of the aortic segment by the blood vessel segment discrimination device,
• generating a depth image for training from the three-dimensional structure of the abdominal surface for training, and
• generating the training data set showing a correspondence relationship between each pixel in the depth image for training and any of a first blood vessel segment corresponding to a Zone 1 of an aorta, a second blood vessel segment corresponding to a Zone 2 of the aorta, a third blood vessel segment corresponding to a Zone 3 of the aorta, and another segment, based on any of the abdominal CT image, the abdominal MRI image, and the abdominal MRA image;
• a three-dimensional structure recognition step of, after the training of the deep learning model is performed using the training data set generated in the training data set generation step, via the three-dimensional structure recognition device, recognizing the three-dimensional structure of the abdomen of the patient;
• a depth image generation step of, via the depth image generation unit, generating the depth image of the abdomen of the patient from the three-dimensional structure of the abdominal surface of the patient recognized in the three-dimensional structure recognition step;
• and
• a blood vessel segment discrimination step of, via the blood vessel segment discrimination device, estimating whether each pixel in the depth image of the abdomen of the patient generated in the depth image generation step corresponds to any of the first blood vessel segment, the second blood vessel segment, the third blood vessel segment, and another segment using the trained deep learning model.

A program for causing a computer constituting a blood vessel segment discrimination device provided in a blood vessel segment discrimination system including:

• a three-dimensional structure recognition device configured to recognize a three-dimensional structure of an abdomen of the patient;
• a depth image generation unit configured to generate a depth image of the abdomen of the patient from the three-dimensional structure of the abdominal surface of the patient recognized by the three-dimensional structure recognition device;
• and
• a training data set generation unit configured to generate a training data set used for training of a deep learning model, to execute:
• a training step of performing the training of the deep learning model using the training data set generated by the training data set generation unit;
• and
• a blood vessel segment discrimination step,
• wherein the training data set generation unit
• generates the three-dimensional structure of the abdominal surface for training from any of an abdominal CT image, an abdominal MRI image, and an abdominal MRA image of a person different from a patient who is a discrimination target of an aortic segment by the blood vessel segment discrimination device,
• generates a depth image for training from the three-dimensional structure of the abdominal surface for training, and generates the training data set showing a correspondence relationship between each pixel in the depth image for training and any of a first blood vessel segment corresponding to a Zone 1of an aorta, a second blood vessel segment corresponding to a Zone 2 of the aorta, a third blood vessel segment corresponding to a Zone 3 of the aorta, and another segment, based on any of the abdominal CT image, the abdominal MRI image, and the abdominal MRA image,
• after the training step is executed, the three-dimensional structure recognition device recognizes the three-dimensional structure of the abdomen of the patient, and the depth image generation unit generates the depth image of the abdomen of the patient from the three-dimensional structure of the abdominal surface of the patient recognized by the three-dimensional structure recognition device, and
• in the blood vessel segment discrimination step, estimation is made as to whether each pixel in the depth image of the abdomen of the patient generated by the depth image generation unit corresponds to any of the first blood vessel segment, the second blood vessel segment, the third blood vessel segment, and another segment using the trained deep learning model.

A blood vessel segment discrimination system, comprising:

• a three-dimensional structure recognition device configured to recognize a three-dimensional structure of an abdomen of the patient;
• a blood vessel segment discrimination device configured to discriminate an aortic segment of the patient whose three-dimensional structure of the abdominal surface is recognized by the three-dimensional structure recognition device, using a deep learning model;
• and
• a training data set generation unit configured to generate a training data set used for training of the deep learning model,
• wherein, before the training of the deep learning model is performed, the training data set generation unit
• generates the three-dimensional structure of the abdominal surface for training from any of an abdominal CT image, an abdominal MRI image, and an abdominal MRA image of a person different from a patient who is a discrimination target of the aortic segment by the blood vessel segment discrimination device, and
• generates the training data set showing a correspondence relationship between each point on the three-dimensional structure of the abdominal surface for training and any of a first blood vessel segment corresponding to a Zone 1 of an aorta, a second blood vessel segment corresponding to a Zone 2 of the aorta, a third blood vessel segment corresponding to a Zone 3 of the aorta, and another segment, based on any of the abdominal CT image, the abdominal MRI image, and the abdominal MRA image, and
• after the training of the deep learning model is performed using the training data set generated by the training data set generation unit,
• the three-dimensional structure recognition device recognizes the three-dimensional structure of the abdomen of the patient, and
• the blood vessel segment discrimination device estimates whether each point on the three-dimensional structure of the abdomen of the patient recognized by the three-dimensional structure recognition device corresponds to any of the first blood vessel segment, the second blood vessel segment, the third blood vessel segment, and another segment using the trained deep learning model.

A blood vessel segment discrimination method for a blood vessel segment discrimination system including:

• a three-dimensional structure recognition device configured to recognize a three-dimensional structure of an abdomen of the patient;
• a blood vessel segment discrimination device configured to discriminate an aortic segment of the patient whose three-dimensional structure of the abdominal surface is recognized by the three-dimensional structure recognition device, using a deep learning model;
• and
• a training data set generation unit configured to generate a training data set used for training of the deep learning model, the blood vessel segment discrimination method comprising:
• a training data set generation step of, before the training of the deep learning model is performed, via the training data set generation unit,
• generating the three-dimensional structure of the abdominal surface for training from any of an abdominal CT image, an abdominal MRI image, and an abdominal MRA image of a person different from a patient who is a discrimination target of the aortic segment by the blood vessel segment discrimination device, and
• generating the training data set showing a correspondence relationship between each point on the three-dimensional structure of the abdominal surface for training and any of a first blood vessel segment corresponding to a Zone 1 of an aorta, a second blood vessel segment corresponding to a Zone 2 of the aorta, a third blood vessel segment corresponding to a Zone 3 of the aorta, and another segment, based on any of the abdominal CT image, the abdominal MRI image, and the abdominal MRA image;
• a three-dimensional structure recognition step of, after the training of the deep learning model is performed using the training data set generated in the training data set generation step, via the three-dimensional structure recognition device, recognizing the three-dimensional structure of the abdomen of the patient;
• and
• a blood vessel segment discrimination step of, via the blood vessel segment discrimination device, estimating whether each point on the three-dimensional structure of the abdomen of the patient recognized in the three-dimensional structure recognition step corresponds to any of the first blood vessel segment, the second blood vessel segment, the third blood vessel segment, and another segment using the trained deep learning model.

A program for causing a computer constituting a blood vessel segment discrimination device provided in a blood vessel segment discrimination system including:

• a three-dimensional structure recognition device configured to recognize a three-dimensional structure of an abdomen of the patient;
• and
• a training data set generation unit configured to generate a training data set used for training of a deep learning model, to execute:
• a training step of performing the training of the deep learning model using the training data set generated by the training data set generation unit;
• and
• a blood vessel segment discrimination step,
• wherein the training data set generation unit
• generates the three-dimensional structure of the abdominal surface for training from any of an abdominal CT image, an abdominal MRI image, and an abdominal MRA image of a person different from a patient who is a discrimination target of an aortic segment by the blood vessel segment discrimination device, and
• generates the training data set showing a correspondence relationship between each point on the three-dimensional structure of the abdominal surface for training and any of a first blood vessel segment corresponding to a Zone 1 of an aorta, a second blood vessel segment corresponding to a Zone 2 of the aorta, a third blood vessel segment corresponding to a Zone 3 of the aorta, and another segment, based on any of the abdominal CT image, the abdominal MRI image, and the abdominal MRA image,
• after the training step is executed, the three-dimensional structure recognition device recognizes the three-dimensional structure of the abdomen of the patient, and
• in the blood vessel segment discrimination step, estimation is made as to whether each point on the three-dimensional structure of the abdomen of the patient recognized by the three-dimensional structure recognition device corresponds to any of the first blood vessel segment, the second blood vessel segment, the third blood vessel segment, and another segment using the trained deep learning model.