PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI
This work addresses pathology diagnosis and medical robot decision systems, but it is incremental as it adapts existing methods to a new domain.
The paper tackles semantic segmentation of multi-channel fMRI for pathology diagnosis by proposing a PCA-aided fully convolutional network, which outperforms other methods even with small training data and achieves inference in 90 milliseconds per dataset.
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.