The candidate multi-cut for cell segmentation

Two successful approaches for the segmentation of biomedical images are (1) the selection of segment candidates from a merge-tree, and (2) the clustering of small superpixels by solving a Multi-Cut problem. In this paper, we introduce a model that unifies both approaches. Our model, the Candidate Mu...

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Bibliographic Details
Main Authors: Funke, Jan, Zhang, Chong, Pietzsch, Tobias, Gonzalez Ballester, Miguel A., Saalfeld, Stephan
Format: Conference Proceeding
Language:English
Subjects:
Electron microscopy
Feature extraction
Image segmentation
Merging
Neurons
Three-dimensional displays
Online Access:Request full text
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Summary:Two successful approaches for the segmentation of biomedical images are (1) the selection of segment candidates from a merge-tree, and (2) the clustering of small superpixels by solving a Multi-Cut problem. In this paper, we introduce a model that unifies both approaches. Our model, the Candidate Multi-Cut (CMC), allows joint selection and clustering of segment candidates from a merge-tree. This way, we overcome the respective limitations of the individual methods: (1) the space of possible segmentations is not constrained to candidates of a merge-tree, and (2) the decision for clustering can be made on candidates larger than superpixels, using features over larger contexts. We solve the optimization problem of selecting and clustering of candidates using an integer linear program. On datasets of 2D light microscopy of cell populations and 3D electron microscopy of neurons, we show that our method generalizes well and generates more accurate segmentations than merge-tree or Multi-Cut methods alone.
ISSN:1945-8452
1945-8452
DOI:10.1109/ISBI.2018.8363658