title: Volumetric Semantic Segmentation using Pyramid Context Features.
abstract: We present an algorithm for the per-voxel semantic segmentation of a three-dimensional volume. At the core of our algorithm is a novel "pyramid context" feature, a descriptive representation designed such that exact per-voxel linear classification can be made extremely efficient. This feature not only allows for efficient semantic segmentation but enables other aspects of our algorithm, such as novel learned features and a stacked architecture that can reason about self-consistency. We demonstrate our technique on 3D fluorescence microscopy data of Drosophila embryos for which we are able to produce extremely accurate semantic segmentations in a matter of minutes, and for which other algorithms fail due to the size and high-dimensionality of the data, or due to the difficulty of the task.
authors.affiliation: UC Berkeley.
authors.forename: Jonathan T
authors.initials: JT
authors.lastname: Barron
authors.affiliation: UC Berkeley.
authors.forename: Pablo
authors.initials: P
authors.lastname: Arbeláez
authors.affiliation: Lawrence Berkeley National Laboratory.
authors.forename: Soile V E
authors.initials: SV
authors.lastname: Keränen
authors.affiliation: Lawrence Berkeley National Laboratory.
authors.forename: Mark D
authors.initials: MD
authors.lastname: Biggin
authors.affiliation: Lawrence Berkeley National Laboratory.
authors.forename: David W
authors.initials: DW
authors.lastname: Knowles
authors.affiliation: UC Berkeley.
authors.forename: Jitendra
authors.initials: J
authors.lastname: Malik