U-Net: Convolutional Networks for Biomedical Image Segmentation

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .Comment: conditionally accepted at MICCAI 201

Probing the Low Surface Brightness Dwarf Galaxy Population of the Virgo Cluster

We have used public data from the Next Generation Virgo Survey (NGVS) to investigate the dwarf galaxy population of the Virgo cluster beyond what has previously been discovered. We initially mask and smooth the data, and then use the object detection algorithm Sextractor to make our initial dwarf galaxy selection. All candidates are then visually inspected to remove artefacts and duplicates. We derive Sextractor parameters to best select low surface brightness galaxies using g band central surface brightness values of 22.5 to 26.0 mag sq arc sec and exponential scale lengths of 3.0 - 10.0 arc sec to identify 443 cluster dwarf galaxies - 303 of which are new detections. These new detections have a surface density that decreases with radius from the cluster centre. We also apply our selection algorithm to 'background', non-cluster, fields and find zero detections. In combination, this leads us to believe that we have isolated a cluster dwarf galaxy population. The range of objects we are able to detect is limited because smaller scale sized galaxies are confused with the background, while larger galaxies are split into numerous smaller objects by the detection algorithm. Using data from previous surveys combined with our data, we find a faint end slope to the luminosity function of -1.35+/-0.03, which does not significantly differ to what has previously been found for the Virgo cluster, but is a little steeper than the slope for field galaxies. There is no evidence for a faint end slope steep enough to correspond with galaxy formation models, unless those models invoke either strong feedback processes or use warm dark matter.Comment: Accepted for publication in MNRA