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18 Publications

Showing 1-10 of 18 results
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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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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02/01/16 | Automated detection and quantification of single RNAs at cellular resolution in zebrafish embryos.
Stapel LCarine, 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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11/01/14 | Active graph matching for automatic joint segmentation and annotation of C. elegans.
Kainmueller D, Jug F, Rother C, Myers G
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2014;17(Pt 1):81-8

In this work we present a novel technique we term active graph matching, which integrates the popular active shape model into a sparse graph matching problem. This way we are able to combine the benefits of a global, statistical deformation model with the benefits of a local deformation model in form of a second-order random field. We present a new iterative energy minimization technique which achieves empirically good results. This enables us to exceed state-of-the art results for the task of annotating nuclei in 3D microscopic images of C. elegans. Furthermore with the help of the generalized Hough transform we are able to jointly segment and annotate a large set of nuclei in a fully automatic fashion for the first time.

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09/14/14 | Tracking by assignment facilitates data curation.
Jug F, Tobias Pietzsch , Kainmueller D, Myers EW
Medical Image Computing and Computer-Assisted Intervention – MICCAI Workshop 2014. 2014 Sep 14:

Object tracking is essential for a multitude of biomedical re- search projects. Automated methods are desired in order to avoid im- possible amounts of manual tracking efforts. However, automatically found solutions are not free of errors, and these errors again have to be identified and resolved manually. We propose six innovative ways for semi-automatic curation of automatically found tracking solutions. Respective user interactions are six simple operations: Inclusion and ex- clusion of objects and tracking decisions, specification of the number of objects, and one-click altering of object segmentations. We show how all proposed interactions can be elegantly incorporated into “assignment models” [1,2,3,4,5,6], an innovative and increasingly popular tracking paradigm. Given some user interaction, the tracking engine is capable of computing the respective globally optimal tracking solution efficiently, even benefitting from “warm start”-capabilities. We show that after in- teractively pointing at a single mistake, multiple segmentation and track- ing errors can be fixed automatically in one single re-evaluation, provably leading to the new, feedback-conscious global optimum. 

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12/14/12 | Omnidirectional displacements for deformable surfaces.
Kainmueller D, Lamecker H, Heller MO, Weber B, Hege H, Zachow S
Medical image analysis. 2013 May;17(4):429-41. doi: 10.1016/j.media.2012.11.006

Deformable surface models are often represented as triangular meshes in image segmentation applications. For a fast and easily regularized deformation onto the target object boundary, the vertices of the mesh are commonly moved along line segments (typically surface normals). However, in case of high mesh curvature, these lines may not intersect with the target boundary at all. Consequently, certain deformations cannot be achieved. We propose omnidirectional displacements for deformable surfaces (ODDS) to overcome this limitation. ODDS allow each vertex to move not only along a line segment but within the volumetric inside of a surrounding sphere, and achieve globally optimal deformations subject to local regularization constraints. However, allowing a ball-shaped instead of a linear range of motion per vertex significantly increases runtime and memory. To alleviate this drawback, we propose a hybrid approach, fastODDS, with improved runtime and reduced memory requirements. Furthermore, fastODDS can also cope with simultaneous segmentation of multiple objects. We show the theoretical benefits of ODDS with experiments on synthetic data, and evaluate ODDS and fastODDS quantitatively on clinical image data of the mandible and the hip bones. There, we assess both the global segmentation accuracy as well as local accuracy in high curvature regions, such as the tip-shaped mandibular coronoid processes and the ridge-shaped acetabular rims of the hip bones.

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10/01/12 | Automatic detection and classification of teeth in CT data.
Duy NThe, Lamecker H, Kainmueller D, Zachow S
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2012;15(Pt 1):609-16

We propose a fully automatic method for tooth detection and classification in CT or cone-beam CT image data. First we compute an accurate segmentation of the maxilla bone. Based on this segmentation, our method computes a complete and optimal separation of the row of teeth into 16 subregions and classifies the resulting regions as existing or missing teeth. This serves as a prerequisite for further individual tooth segmentation. We show the robustness of our approach by providing extensive validation on 43 clinical head CT scans.

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08/19/11 | Automatic extraction of mandibular nerve and bone from cone-beam CT data.
Kainmueller D, Lamecker H, Seim H, Zinser M, Zachow S
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2009;12(Pt 2):76-83

The exact localization of the mandibular nerve with respect to the bone is important for applications in dental implantology and maxillofacial surgery. Cone beam computed tomography (CBCT), often also called digital volume tomography (DVT), is increasingly utilized in maxillofacial or dental imaging. Compared to conventional CT, however, soft tissue discrimination is worse due to a reduced dose. Thus, small structures like the alveolar nerves are even harder recognizable within the image data. We show that it is nonetheless possible to accurately reconstruct the 3D bone surface and the course of the nerve in a fully automatic fashion, with a method that is based on a combined statistical shape model of the nerve and the bone and a Dijkstra-based optimization procedure. Our method has been validated on 106 clinical datasets: the average reconstruction error for the bone is 0.5 +/- 0.1 mm, and the nerve can be detected with an average error of 1.0 +/- 0.6 mm.

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09/24/10 | Improving deformable surface meshes through omni-directional displacements and MRFs.
Kainmueller D, Lamecker H, Seim H, Zachow S, Hege HC
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2010;13(Pt 1):227-34

Deformable surface models are often represented as triangular meshes in image segmentation applications. For a fast and easily regularized deformation onto the target object boundary, the vertices of the mesh are commonly moved along line segments (typically surface normals). However, in case of high mesh curvature, these lines may intersect with the target boundary at "non-corresponding" positions, or even not at all. Consequently, certain deformations cannot be achieved. We propose an approach that allows each vertex to move not only along a line segment, but within a surrounding sphere. We achieve globally regularized deformations via Markov Random Field optimization. We demonstrate the potential of our approach with experiments on synthetic data, as well as an evaluation on 2 x 106 coronoid processes of the mandible in Cone-Beam CTs, and 56 coccyxes (tailbones) in low-resolution CTs.

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09/24/10 | Model-based auto-segmentation of knee bones and cartilage in MRI data.
Seim H, Kainmueller D, Lamecker H, Bindernagel M, Malinowski J, Zachow S
Medical Image Analysis for the Clinic - A Grand Challenge, MICCAI 2010, the 13th International Conference on Medical Image Computing and Computer Assisted Intervention. 2010 Sep 24:

We present a method for fully automatic segmentation of the bones and cartilages of the human knee from MRI data. Based on statistical shape models and graph-based optimization, first the femoral and tibial bone surfaces are reconstructed. Starting from the bone sur- faces the cartilages are segmented simultaneously with a multi object technique using prior knowledge on the variation of cartilage thickness. We validate our method on 40 clinical MRI datasets acquired before knee replacement. 

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