Dataset Information

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.

SUBMITTER: Barron JT

PROVIDER: S-EPMC4445881 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Publications

Volumetric Semantic Segmentation using Pyramid Context Features.

Barron Jonathan T JT Arbeláez Pablo P Keränen Soile V E SV Biggin Mark D MD Knowles David W DW Malik Jitendra J

Proceedings. IEEE International Conference on Computer Vision 20131201

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 demonstra ...[more]

PMID: 26029008

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Dataset Information

Volumetric Semantic Segmentation using Pyramid Context Features.

Publications

Volumetric Semantic Segmentation using Pyramid Context Features.

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