A deep learning-based seizure focus three-dimensional automatic positioning system, comprising: a PET image collection and marking module; a PET image and standard symmetrical brain template registration module; a PET image data pre-processing module, which generates left and right brain image block mirror pairs; a siamese network training module, which includes two deep residual convolutional neural networks sharing a weight parameter, wherein an output layer is connected to a multilayer perceptron and a softmax layer, and training set pairs having seizure focus images and normal images are used to train the networks to obtain a network model; a classification module and a seizure focus positioning module, wherein the trained network model is used to generate a probability heat map for a newly inputted PET image, a classifier determines whether the image is normal or has a seizure focus sample, and the position of a seizure focus area is predicted. The system automatically positions seizure foci of PET images via the introduction of image block mirror pairs and by means of the siamese network, can effectively increase seizure focus positioning accuracy and efficiency, and has high robustness.
DEEP LEARNING-BASED SEIZURE FOCUS THREE-DIMENSIONAL AUTOMATIC POSITIONING SYSTEM