  Semantic segmentation of functional magnetic resonance imaging (fMRI) makes
great sense for pathology diagnosis and decision system of medical robots. The
multi-channel fMRI provides more information of the pathological features. But
the increased amount of data causes complexity in feature detections. This
paper proposes a principal component analysis (PCA)-aided fully convolutional
network to particularly deal with multi-channel fMRI. We transfer the learned
weights of contemporary classification networks to the segmentation task by
fine-tuning. The results of the convolutional network are compared with various
methods e.g. k-NN. A new labeling strategy is proposed to solve the semantic
segmentation problem with unclear boundaries. Even with a small-sized training
dataset, the test results demonstrate that our model outperforms other
pathological feature detection methods. Besides, its forward inference only
takes 90 milliseconds for a single set of fMRI data. To our knowledge, this is
the first time to realize pixel-wise labeling of multi-channel magnetic
resonance image using FCN.
