title: Convolutional networks can learn to generate affinity graphs for image segmentation.
abstract: Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.
authors.forename: Srinivas C
authors.initials: SC
authors.lastname: Turaga
authors.forename: Joseph F
authors.initials: JF
authors.lastname: Murray
authors.forename: Viren
authors.initials: V
authors.lastname: Jain
authors.forename: Fabian
authors.initials: F
authors.lastname: Roth
authors.forename: Moritz
authors.initials: M
authors.lastname: Helmstaedter
authors.forename: Kevin
authors.initials: K
authors.lastname: Briggman
authors.forename: Winfried
authors.initials: W
authors.lastname: Denk
authors.forename: H Sebastian
authors.initials: HS
authors.lastname: Seung
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008432:Mathematical Computing; D055641:Mathematical Concepts; D008433:Mathematics; D008854:Microscopy, Electron / Q000379:methods; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated