The present invention relates to a method of analyzing PET (positron emission tomography) for diagnosis of dementia, and more specifically, sagittal plane and coronal view from an axial plane image obtained from a PET image of a axial plane of the brain. After automatically generating a coronal plane image, the artificial intelligence learning results are used for these three cross-section images to automatically classify and analyze normal, early, middle, late or normal, dementia stages for dementia. It relates to a deep learning-based PET image analysis method for diagnosis and prediction of dementia. In the present invention, a plurality of axial plane images are generated by taking a single layer of the axial plane of the brain, and then image interpolation is performed on the plurality of axial plane images to generate a 3D model, and the 3D model Sampling the sagittal and coronal planes at regular intervals to generate a plurality of sagittal and multiple coronal images and performing learning using a machine learning method. A PET image analysis method comprising a reasoner performing a diagnosis of dementia performing a process of inferring a degree, Providing a plurality of predetermined axial plane images; Generating a predetermined sagittal image and a coronal image, respectively, based on the predetermined axial plane image using a 3D model generator; The predetermined axial plane image, the predetermined sagittal plane image, and the predetermined coronal plane image are provided to an axial plane inference machine, a sagittal plane inference machine, and a coronal plane inference machine, and the axial plane inference machine, the sagittal plane inference machine, and And performing a diagnosis of dementia by inputting the results of the coronal inference machine into the selector, Normal, initial, and dementia output values for the axial image , , Define each as Normal, initial, and dementia output values for the sagittal image , , Define each as Normal, initial, and dementia output values for the coronal image , , If each is defined as, The average values of the output values of the axial plane image, the sagittal plane image, and the coronal plane image are calculated as in the following equations (1), (2), (3), (One) (2) (3) Diagnosis result is calculated based on the following formula (4). (4) here, As the final diagnosis result, 0 is normal, 1 is early, and 2 is dementia. max() is characterized by being the maximum value among the elements. When performing the PET image analysis method based on deep learning for diagnosis and prediction of dementia according to the present invention, there is an advantage that more three-dimensional and precise dementia diagnosis is possible compared to the conventional diagnosis method based on only a specific image of the brain.
展开▼
