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Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images.

ABSTRACT: Analysis of microscopy images can provide insight into many biological processes. One particularly challenging problem is cellular nuclear segmentation in highly anisotropic and noisy 3D image data. Manually localizing and segmenting each and every cellular nucleus is very time-consuming, which remains a bottleneck in large-scale biological experiments. In this work, we present a tool for automated segmentation of cellular nuclei from 3D fluorescent microscopic data. Our tool is based on state-of-the-art image processing and machine learning techniques and provides a user-friendly graphical user interface. We show that our tool is as accurate as manual annotation and greatly reduces the time for the registration.

SUBMITTER: Widmer C

PROVIDER: S-EPMC4389647 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images.

Widmer Christian C Heinrich Stephanie S Drewe Philipp P Lou Xinghua X Umrania Shefali S Rätsch Gunnar G

Signal, image and video processing 20141201 1 Suppl

Analysis of microscopy images can provide insight into many biological processes. One particularly challenging problem is cellular nuclear segmentation in highly anisotropic and noisy 3D image data. Manually localizing and segmenting each and every cellular nucleus is very time-consuming, which remains a bottleneck in large-scale biological experiments. In this work, we present a tool for automated segmentation of cellular nuclei from 3D fluorescent microscopic data. Our tool is based on state-o ...[more]

PMID: 25866587

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