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3 Publications

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    Kainmueller Lab
    09/19/16 | Mapping auto-context decision forests to deep ConvNets for semantic segmentation.
    Richmond DL, Kainmueller D, Yang MY, Myers EW, Rother C
    British Machine Vision Conference. 2016 Sep 19:

    In this paper, we propose a mapping from the Auto-context model to a deep Convolutional Neural Network (ConvNet), bridging the gap be- tween these two models, and helping address the challenge of training ConvNets with limited training data. 

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    Kainmueller Lab
    06/27/16 | Convexity shape constraints for image segmentation.
    Royer LA, Richmond DL, Rother C, Andres B, Kainmueller D
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016 Jun 27:. doi: 10.1109/CVPR.2016.50

    Segmenting an image into multiple components is a central task in computer vision. In many practical scenarios, prior knowledge about plausible components is available. Incorporating such prior knowledge into models and algorithms for image segmentation is highly desirable, yet can be non-trivial. In this work, we introduce a new approach that allows, for the first time, to constrain some or all components of a segmentation to have convex shapes. Specifically, we extend the Minimum Cost Multicut Problem by a class of constraints that enforce convexity. To solve instances of this NP-hard integer linear program to optimality, we separate the proposed constraints in the branch-and-cut loop of a state-of-the-art ILP solver. Results on photographs and micrographs demonstrate the effectiveness of the approach as well as its advantages over the state-of-the-art heuristic.

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    Kainmueller Lab
    02/01/16 | Automated detection and quantification of single RNAs at cellular resolution in zebrafish embryos.
    Stapel LC, Lombardot B, Broaddus C, Kainmueller D, Jug F, Myers EW, Vastenhouw NL
    Development (Cambridge, England). 2016 Feb 01;143(3):540-6. doi: 10.1242/dev.128918

    Analysis of differential gene expression is crucial for the study of cell fate and behavior during embryonic development. However, automated methods for the sensitive detection and quantification of RNAs at cellular resolution in embryos are lacking. With the advent of single-molecule fluorescence in situ hybridization (smFISH), gene expression can be analyzed at single-molecule resolution. However, the limited availability of protocols for smFISH in embryos and the lack of efficient image analysis pipelines have hampered quantification at the (sub)cellular level in complex samples such as tissues and embryos. Here, we present a protocol for smFISH on zebrafish embryo sections in combination with an image analysis pipeline for automated transcript detection and cell segmentation. We use this strategy to quantify gene expression differences between different cell types and identify differences in subcellular transcript localization between genes. The combination of our smFISH protocol and custom-made, freely available, analysis pipeline will enable researchers to fully exploit the benefits of quantitative transcript analysis at cellular and subcellular resolution in tissues and embryos.

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