Funke J, Zhang C, Tobias P, Gonzalez Ballester MA, Saalfeld S. The Candidate Multi-Cut for Cell Segmentation. IEEE International Symposium on Biomedical Imaging (ISBI'18)
We develop a large number of software tools and hosting infrastructures to support the research developed at the Department. We will be detailing in this section the different tools available. You can take a look for the moment at the offer available within the UPF Knowledge Portal, the innovations created in the context of EU projects in the Innovation Radar and the software sections of some of our research groups:
Artificial Intelligence |
Nonlinear Time Series Analysis |
Web Research |
Music Technology |
Interactive Technologies |
Barcelona MedTech |
Natural Language Processing |
Nonlinear Time Series Analysis |
UbicaLab |
Wireless Networking |
Educational Technologies |
Funke J, Zhang C, Tobias P, Gonzalez Ballester MA, Saalfeld S. The Candidate Multi-Cut for Cell Segmentation. IEEE International Symposium on Biomedical Imaging (ISBI'18)
Funke J, Zhang C, Tobias P, Gonzalez Ballester MA, Saalfeld S. The Candidate Multi-Cut for Cell Segmentation. IEEE International Symposium on Biomedical Imaging (ISBI'18)
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.
Additional material: