Brain fMRI segmentation under emotion stimuli incorporating attention-based deep convolutional neural networks

Abstract

Functional magnetic resonance imaging (fMRI) is widely used for clinical examinations, diagnosis, and treatment. By segmenting fMRI images, large-scale medical image data can be processed more efficiently. Most deep learning (DL)-based segmentation typically uses some type of encoding–decoding model. In this study, affective computing (AC) was developed using the brain fMRI dataset generated from an emotion simulation experiment. The brain fMRI dataset was segmented using an attention model, a deep convolutional neural network-32 (DCNN-32) based on Laplacian of Gaussian (LoG) filter, called ADCNN-32-G. For the evaluation of image segmentation, several indices are presented. By comparing the proposed ADCNN-32s-G model to distance regularized level set evolution (DRLSE), single-seeded region growing, and the single segNet full convolutional network model (FCN), the proposed model performs well in segmenting mass fMRI datasets. The proposed method can be applied to the real-time monitoring of patients with depression, and it can effectively advise human mental health.