@conference {Schlesinger2017,
	title = {Crowd sourcing image segmentation with iaSTAPLE},
	booktitle = {Proceedings - International Symposium on Biomedical Imaging},
	year = {2017},
	pages = {401{\textendash}405},
	abstract = {We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware STAPLE (iaSTAPLE) since our image segmentation model seamlessly integrates into the well-known and widely used STAPLE approach. In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99\% of all cells when compared to expert segmentations. These results show that iaSTAPLE is a highly useful tool for crowd sourcing image segmentation.},
	keywords = {Crowdsourcing, Epithelial cell segmentation, IaSTAPLE, Markovian Random Fields},
	isbn = {9781509011711},
	issn = {19458452},
	doi = {10.1109/ISBI.2017.7950547},
	author = {Schlesinger, Dmitrij and Jug, Florian and Myers, Gene and Carsten Rother and Kainmueller, Dagmar}
}
@incollection {Richmond2015,
	title = {Uncertainty-driven forest predictors for vertebra localization and segmentation},
	volume = {9349},
	year = {2015},
	pages = {653{\textendash}660},
	abstract = {Accurate localization, identification and segmentation of vertebrae is an important task in medical and biological image analysis. The prevailing approach to solve such a task is to first generate pixelindependent features for each vertebra, e.g. via a random forest predictor, which are then fed into an MRF-based objective to infer the optimal MAP solution of a constellation model. We abandon this static, twostage approach and mix feature generation with model-based inference in a new, more flexible, way. We evaluate our method on two data sets with different objectives. The first is semantic segmentation of a 21-part body plan of zebrafish embryos in microscopy images, and the second is localization and identification of vertebrae in benchmark human CT.},
	issn = {16113349},
	doi = {10.1007/978-3-319-24553-9_80},
	author = {Richmond, David and Kainmueller, Dagmar and Glocker, Ben and Carsten Rother and Myers, Gene}
}
@conference {Kainmueller2014,
	title = {Active graph matching for automatic joint segmentation and annotation of C. elegans},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8673 LNCS},
	number = {PART 1},
	year = {2014},
	pages = {81{\textendash}88},
	abstract = {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. {\textcopyright} 2014 Springer International Publishing.},
	isbn = {9783319104034},
	issn = {16113349},
	doi = {10.1007/978-3-319-10404-1_11},
	author = {Kainmueller, Dagmar and Jug, Florian and Carsten Rother and Myers, Gene}
}
@article {Jug2014,
	title = {Optimal joint segmentation and tracking of escherichia coli in the mother machine},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8677},
	year = {2014},
	pages = {25{\textendash}36},
	abstract = {We introduce a graphical model for the joint segmentation and tracking of E. coli cells from time lapse videos. In our setup cells are grown in narrow columns (growth channels) in a so-called {\textquotedblleft}Mother Machine{\textquotedblright} [1]. In these growth channels, cells are vertically aligned, grow and divide over time, and eventually leave the channel at the top. The model is built on a large set of cell segmentation hypotheses for each video frame that we extract from data using a novel parametric max-flow variation. Possible tracking assignments between segments across time, including cell identity mapping, cell division, and cell exit events are enumerated. Each such assignment is represented as a binary decision variable with unary costs based on image and object features of the involved segments. We find a cost-minimal and consistent solution by solving an integer linear program. We introduce a new and important type of constraint that ensures that cells exit the Mother Machine in the correct order. Our method finds a globally optimal tracking solution with an accuracy of > 95\% (1.22 times the inter-observer error) and is on average 2 - 11 times faster than the microscope produces the raw data.},
	issn = {16113349},
	doi = {10.1007/978-3-319-12289-2_3},
	author = {Jug, Florian and Pietzsch, Tobias and Kainm{\"u}ller, Dagmar and Funke, Jan and Kaiser, Matthias and van Nimwegen, Erik and Carsten Rother and Myers, Gene}
}
