We demonstrate the application of the u-net to three different segmentation tasks.
The first task is the segmentation of neuronal structures in electron microscopic recordings. An example of the data set and our obtained segmentation is displayed in Figure 2. We provide the full result as Supplementary Material.
The data set is provided by the EM segmentation challenge  that was started at ISBI 2012 and is still open for new contributions. The training data is a set of 30 images (512x512 pixels) from serial section transmission electron microscopy of the Drosophila first instar larva ventral nerve cord (VNC). Each image comes with a corresponding fully annotated ground truth segmentation map for cells (white) and membranes (black).
The test set is publicly available, but its segmentation maps are kept secret.
An evaluation can be obtained by sending the predicted membrane probability map to the organizers. The evaluation is done by thresholding the map at 10 different levels and computation of the “warping error”, the “Rand error” and the “pixel error” .
包含了三種誤差評估方式：warping error, Rand error, and pixel error
但是看了官方網站，目前Pixel error 與 rand error 已經被棄用。