Fully Convolutional Networks for Semantic Segmentation of Fluorescence Microscopies
We experiment with fully convolutional neural networks "FCNs" that discard the traditional fully connected layers of a ConvNet, in order to produce a pixel wise class prediction and loss. While our fully convolutional network is ultimately unable to perform accurate semantic segmentation of dendritic spine images, we demonstrate that a model, consisting of a joint sliding window regionproposal network and SVM classifier, is capable of accurately predict the location of dendritic spines in microscopies of stained mouse neurons. Our model holds the potential to change the face of one sector of neuroscience research by replacing laborious hand annotation of dendritic spine structures with automatic detection.