@article {68587, title = {Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.}, journal = {Nature Biotechnology}, volume = {41}, year = {2023}, month = {2023 Jan 01}, pages = {44-49}, abstract = {

We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8\% of cell lineages spanning 1 h, as compared to 31.8\% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.

}, keywords = {Animals, Blastocyst, Cell Lineage, Embryo, Mammalian, Mice, Microscopy}, issn = {1546-1696}, doi = {10.1038/s41587-022-01427-7}, author = {Malin-Mayor, Caroline and Hirsch, Peter and Guignard, Leo and McDole, Katie and Wan, Yinan and Lemon, William C and Kainmueller, Dagmar and Keller, Philipp J and Preibisch, Stephan and Funke, Jan} } @article {68629, title = {A searchable image resource of Drosophila GAL4-driver expression patterns with single neuron resolution.}, journal = {eLife}, volume = {12}, year = {2023}, month = {2023 Feb 23}, abstract = {

Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.

}, issn = {2050-084X}, doi = {10.7554/eLife.80660}, author = {Meissner, Geoffrey Wilson and Nern, Aljoscha and Dorman, Zachary and Depasquale, Gina M and Forster, Kaitlyn and Gibney, Theresa and Hausenfluck, Joanna H and He, Yisheng and Iyer, Nirmala A and Jeter, Jennifer and Johnson, Lauren and Johnston, Rebecca M and Lee, Kelley and Melton, Brian and Yarbrough, Brianna and Zugates, Christopher T and Clements, Jody and Goina, Cristian and Otsuna, Hideo and Rokicki, Konrad and Svirskas, Robert R and Aso, Yoshinori and Card, Gwyneth M and Dickson, Barry J and Ehrhardt, Erica and Goldammer, Jens and Ito, Masayoshi and Kainmueller, Dagmar and Korff, Wyatt and Mais, Lisa and minegishi, ryo and Namiki, Shigehiro and Rubin, Gerald M and Sterne, Gabriella R and Wolff, Tanya and Malkesman, Oz} } @article {68313, title = {Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.}, journal = {Nature Biotechnology}, year = {2022}, month = {2022 Sep 05}, abstract = {

We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8\% of cell lineages spanning 1 h, as compared to 31.8\% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.

}, issn = {1546-1696}, doi = {10.1038/s41587-022-01427-7}, author = {Malin-Mayor, Caroline and Hirsch, Peter and Guignard, Leo and McDole, Katie and Wan, Yinan and Lemon, William C and Kainmueller, Dagmar and Keller, Philipp J and Preibisch, Stephan and Funke, Jan} } @article {68507, title = {A connectome and analysis of the adult Drosophila central brain.}, journal = {Elife}, volume = {9}, year = {2020}, month = {2020 Sep 07}, abstract = {

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly . Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly{\textquoteright}s brain.

}, keywords = {Animals, Brain, Connectome, Drosophila melanogaster, Female, Male, Neurons, Synapses}, issn = {2050-084X}, doi = {10.7554/eLife.57443}, author = {Scheffer, Louis K and Xu, C Shan and Januszewski, Michal and Lu, Zhiyuan and Takemura, Shin-ya and Hayworth, Kenneth J and Huang, Gary B and Shinomiya, Kazunori and Maitlin-Shepard, Jeremy and Berg, Stuart and Clements, Jody and Hubbard, Philip M and Katz, William T and Umayam, Lowell and Zhao, Ting and Ackerman, David and Blakely, Tim and Bogovic, John and Dolafi, Tom and Kainmueller, Dagmar and Kawase, Takashi and Khairy, Khaled A and Leavitt, Laramie and Li, Peter H and Lindsey, Larry and Neubarth, Nicole and Olbris, Donald J and Otsuna, Hideo and Trautman, Eric T and Ito, Masayoshi and Bates, Alexander S and Goldammer, Jens and Wolff, Tanya and Svirskas, Robert and Schlegel, Philipp and Neace, Erika and Knecht, Christopher J and Alvarado, Chelsea X and Bailey, Dennis A and Ballinger, Samantha and Borycz, Jolanta A and Canino, Brandon S and Cheatham, Natasha and Cook, Michael and Dreher, Marisa and Duclos, Octave and Eubanks, Bryon and Fairbanks, Kelli and Finley, Samantha and Forknall, Nora and Francis, Audrey and Hopkins, Gary Patrick and Joyce, Emily M and Kim, SungJin and Kirk, Nicole A and Kovalyak, Julie and Lauchie, Shirley A and Lohff, Alanna and Maldonado, Charli and Manley, Emily A and McLin, Sari and Mooney, Caroline and Ndama, Miatta and Ogundeyi, Omotara and Okeoma, Nneoma and Ordish, Christopher and Padilla, Nicholas and Patrick, Christopher M and Paterson, Tyler and Phillips, Elliott E and Phillips, Emily M and Rampally, Neha and Ribeiro, Caitlin and Robertson, Madelaine K and Rymer, Jon Thomson and Ryan, Sean M and Sammons, Megan and Scott, Anne K and Scott, Ashley L and Shinomiya, Aya and Smith, Claire and Smith, Kelsey and Smith, Natalie L and Sobeski, Margaret A and Suleiman, Alia and Swift, Jackie and Takemura, Satoko and Talebi, Iris and Tarnogorska, Dorota and Tenshaw, Emily and Tokhi, Temour and Walsh, John J and Yang, Tansy and Horne, Jane Anne and Li, Feng and Parekh, Ruchi and Rivlin, Patricia K and Jayaraman, Vivek and Costa, Marta and Jefferis, Gregory Sxe and Ito, Kei and Saalfeld, Stephan and George, Reed and Meinertzhagen, Ian A and Rubin, Gerald M and Hess, Harald F and Jain, Viren and Plaza, Stephen M} } @article {48163, title = {Automated detection and quantification of single RNAs at cellular resolution in zebrafish embryos.}, journal = {Development (Cambridge, England)}, volume = {143}, year = {2016}, month = {2016 Feb 01}, pages = {540-6}, abstract = {

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.

}, keywords = {Animals, Automation, Cell Membrane, Embryo, Nonmammalian, Gene Expression Regulation, Developmental, In Situ Hybridization, Fluorescence, RNA, RNA, Messenger, Subcellular Fractions, Transcription, Genetic, Zebrafish}, issn = {1477-9129}, doi = {10.1242/dev.128918}, author = {Stapel, L Carine and Lombardot, Benoit and Broaddus, Coleman and Kainmueller, Dagmar and Jug, Florian and Myers, Eugene W and Vastenhouw, Nadine L} } @conference {48179, title = {Convexity shape constraints for image segmentation.}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2016}, month = {2016 Jun 27}, publisher = {IEEE}, organization = {IEEE}, address = {Las Vegas, NV}, abstract = {

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.

}, doi = {10.1109/CVPR.2016.50}, author = {Royer, Lo{\"\i}c A and Richmond, David L and Rother, Carsten and Andres, Bjoern and Kainmueller, Dagmar} } @conference {48178, title = {Mapping auto-context decision forests to deep ConvNets for semantic segmentation.}, booktitle = {British Machine Vision Conference}, year = {2016}, month = {2016 Sep 19}, address = {York}, abstract = {

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.\ 

}, url = {http://www.bmva.org/bmvc/2016/abstracts.pdf}, author = {Richmond, David L and Kainmueller, Dagmar and Yang, Michael Y and Myers, Eugene W and Rother, Carsten} } @article {48164, title = {Active graph matching for automatic joint segmentation and annotation of C. elegans.}, journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {17}, year = {2014}, month = {2014}, pages = {81-8}, 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.

}, keywords = {Algorithms, Animals, Caenorhabditis elegans, Cell Nucleus, Image Enhancement, Image Interpretation, Computer-Assisted, Microscopy, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique}, author = {Kainmueller, Dagmar and Jug, Florian and Rother, Carsten and Myers, Gene} } @conference {48180, title = {Tracking by assignment facilitates data curation.}, booktitle = {Medical Image Computing and Computer-Assisted Intervention {\textendash} MICCAI Workshop 2014}, year = {2014}, month = {2014 Sep 14}, address = {Boston, MA}, abstract = {

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 {\textquotedblleft}assignment models{\textquotedblright} [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 {\textquotedblleft}warm start{\textquotedblright}-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.\ 

}, url = {http://interactivemedical.org/imic2014/CameraReadyPapers/Paper\%203/Jug_Interactive_IMIC2014.pdf}, author = {Jug, Florian and Tobias Pietzsch and Kainmueller, Dagmar and Myers, Eugene W} } @article {48165, title = {Omnidirectional displacements for deformable surfaces.}, journal = {Medical image analysis}, volume = {17}, year = {2013}, month = {2013 May}, pages = {429-41}, abstract = {

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.

}, keywords = {Algorithms, Computer Simulation, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Models, Biological, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity}, issn = {1361-8423}, doi = {10.1016/j.media.2012.11.006}, author = {Kainmueller, Dagmar and Lamecker, Hans and Heller, Markus O and Weber, Britta and Hege, Hans-Christian and Zachow, Stefan} } @article {48166, title = {Automatic detection and classification of teeth in CT data.}, journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {15}, year = {2012}, month = {2012}, pages = {609-16}, abstract = {

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.

}, keywords = {Algorithms, Artifacts, Bone and Bones, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Maxilla, Models, Statistical, Pattern Recognition, Automated, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Tomography, X-Ray Computed, Tooth}, author = {Duy, Nguyen The and Lamecker, Hans and Kainmueller, Dagmar and Zachow, Stefan} } @conference {48181, title = {Model-based auto-segmentation of knee bones and cartilage in MRI data.}, booktitle = {Medical Image Analysis for the Clinic - A Grand Challenge, MICCAI 2010, the 13th International Conference on Medical Image Computing and Computer Assisted Intervention}, year = {2010}, month = {2010 Sep 24}, address = {Beijing, China}, abstract = {

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.\ 

}, url = {http://www.ski10.org/data/2010-11-09-1131.pdf}, author = {Seim, Heiko and Kainmueller, Dagmar and Lamecker, Hans and Bindernagel, Matthias and Malinowski, Jana and Zachow, Stefan} } @article {48169, title = {An articulated statistical shape model for accurate hip joint segmentation.}, journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference}, volume = {2009}, year = {2009}, month = {2009}, pages = {6345-51}, abstract = {

In this paper we propose a framework for fully automatic, robust and accurate segmentation of the human pelvis and proximal femur in CT data. We propose a composite statistical shape model of femur and pelvis with a flexible hip joint, for which we extend the common definition of statistical shape models as well as the common strategy for their adaptation. We do not analyze the joint flexibility statistically, but model it explicitly by rotational parameters describing the bent in a ball-and-socket joint. A leave-one-out evaluation on 50 CT volumes shows that image driven adaptation of our composite shape model robustly produces accurate segmentations of both proximal femur and pelvis. As a second contribution, we evaluate a fine grain multi-object segmentation method based on graph optimization. It relies on accurate initializations of femur and pelvis, which our composite shape model can generate. Simultaneous optimization of both femur and pelvis yields more accurate results than separate optimizations of each structure. Shape model adaptation and graph based optimization are embedded in a fully automatic framework.

}, keywords = {Algorithms, Artificial Intelligence, Computer Simulation, Hip Joint, Imaging, Three-Dimensional, Models, Anatomic, Models, Statistical, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Sensitivity and Specificity, Tomography, X-Ray Computed}, issn = {1557-170X}, doi = {10.1109/IEMBS.2009.5333269}, author = {Kainmueller, Dagmar and Lamecker, Hans and Zachow, Stefan and Hege, Hans-Christian} } @article {48176, title = {Automatic Extraction of Anatomical Landmarks From Medical Image Data: An Evaluation of Different Methods}, year = {2009}, abstract = {

This work presents three different methods for automatic detection of anatomical landmarks in CT data, namely for the left and right anterior superior iliac spines and the pubic symphysis. The methods exhibit different degrees of generality in terms of portability to other anatomical landmarks and require a different amount of training data. The srst method is problem-specisc and is based on the convex hull of the pelvis. Method two is a more generic approach based on a statistical shape model including the landmarks of interest for every training shape. With our third method we present the most generic approach, where only a small set of training landmarks is required. Those landmarks are transferred to the patient specisc geometry based on Mean Value Coordinates (MVCs). The methods work on surfaces of the pelvis that need to be extracted beforehand. We perform this geometry reconstruction with our previously introduced fully automatic segmentation framework for the pelvic bones. With a focus on the accuracy of our novel MVC-based approach, we evaluate and compare our methods on 100 clinical CT datasets, for which gold standard landmarks were desned manually by multiple observers.

}, author = {Kainmueller, Dagmar and Hans-Christian Hege and Heiko Seim and Markus Heller and Stefan Zachow} } @article {48174, title = {Automatic extraction of mandibular nerve and bone from cone-beam CT data.}, journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {12}, year = {2009}, month = {2009}, pages = {76-83}, abstract = {

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.

}, keywords = {Algorithms, Artificial Intelligence, Cone-Beam Computed Tomography, Humans, Mandible, Mandibular Nerve, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique}, author = {Kainmueller, Dagmar and Lamecker, Hans and Seim, Heiko and Zinser, Max and Zachow, Stefan} } @article {48183, title = {Multi-object segmentation of head bones.}, journal = {MIDAS Journal}, year = {2009}, month = {2009 Jul 20}, abstract = {

We present a fully automatic method for 3D segmentation of the mandibular bone from CT data. The method includes an adaptation of statistical shape models of the mandible, the skull base and the midfacial bones, followed by a simultaneous graph-based optimization of adjacent deformable models. The adaptation of the models to the image data is performed according to a heuristic model of the typical intensity distribution in the vincinity of the bone boundary, with special focus on an accurate discrimination of adjacent bones in joint regions. An evaluation of our method based on 18 CT scans shows that a manual correction of the automatic segmentations is not necessary in approx. 60\% of the axial slices that contain the mandible.

}, url = {http://hdl.handle.net/10380/3099}, author = {Kainmueller, Dagmar and Lamecker, Hans and Seim, Heiko and Zachow, Stefan} } @article {48182, title = {Multi-object segmentation with coupled deformable models.}, journal = {Annals of the British Machine Vision Association}, volume = {2009}, year = {2009}, month = {2009}, pages = {1-10}, abstract = {

For biomechanical simulations, the segmentation of multiple adjacent anatomical struc- tures from medical image data is often required. If adjacent structures are barely dis- tinguishable in image data, in general automatic segmentation methods for single struc- tures do not yield sufficiently accurate results. To improve segmentation accuracy in these cases, knowledge about adjacent structures must be exploited. Optimal graph searching (graph cuts) based on deformable surface models allows for a simultaneous segmentation of multiple adjacent objects. However, this method requires a correspon- dence relation between vertices of adjacent surface meshes. Line segments, each con- taining two corresponding vertices, may then serve as shared displacement directions in the segmentation process. In this paper we propose a scheme for constructing a corre- spondence relation in adjacent regions of two arbitrary surfaces. This correspondence relation implies shared displacement directions that we apply for segmentation with de- formable surfaces. Here, overlap of the surfaces is guaranteed not to occur. We show correspondence relations for regions on a femoral head and acetabulum and other adja- cent structures, as well as an evaluation of segmentation results on 50 ct images of the hip joint.\ 

}, url = {http://www.bmva.org/annals/2009/2009-0005.pdf}, author = {Kainmueller, Dagmar and Lamecker, Hans and Zachow, Stefan} } @article {48177, title = {Automatic Segmentation of the Pelvic Bones from CT Data Based on a Statistical Shape Model}, year = {2008}, abstract = {

We present an algorithm for automatic segmentation of the human pelvic bones from CT datasets that is based on the application of a statistical shape model. The proposed method is divided into three steps: 1) The averaged shape of the pelvis model is initially placed within the CT data using the Generalized Hough Transform, 2) the statistical shape model is then adapted to the image data by a transformation and variation of its shape modes, and 3) a final free-form deformation step based on optimal graph searching is applied to overcome the restrictive character of the statistical shape representation. We thoroughly evaluated the method on 50 manually segmented CT datasets by performing a leave-one-out study. The Generalized Hough Transform proved to be a reliable method for an automatic initial placement of the shape model within the CT data. Compared to the manual gold standard segmentations, our automatic segmentation approach produced an average surface distance of 1.2 {\textpm} 0.3mm after the adaptation of the statistical shape model, which could be reduced to 0.7{\textpm}0.3mm using a final free-form deformation step. Together with an average segmentation time of less than 5 minutes, the results of our study indicate that our method meets the requirements of clinical routine.

}, doi = {10.2312/VCBM/VCBM08/093-100}, author = {Kainmueller, Dagmar and Seim, Heiko and Heller, Markus and Lamecker, Hans and Zachow, Stefan and Hege, Hans-Christian} } @conference {48184, title = {Model-based autosegmentation of the central brain of the honeybee, Apis mellifera, using active statistical shape models.}, booktitle = {Neuroinformatics 2008}, year = {2008}, month = {2008 Sep 07}, address = {Stockholm, Sweden}, abstract = {

The Honeybee Brain Atlas serves as 3D database and communicative platform to accumulate structural data, i.e. reconstructed neurons, derived from confocal scans (Brandt et al., 2005) (www.neurobiologie.fu-berlin.de/beebrain/) (1). Transforming neurons into the atlas requires manual segmentation of neuropils within confocal images, a time-consuming task requiring expertise in identifying biological structures which can result in different outcomes from various segmenters.

}, doi = {10.3389/conf.neuro.11.2008.01.064}, author = {Singer, Jochen and Lienhard, Matthias and Seim, Heiko and Kainmueller, Dagmar and Kuss, Anja and Lamecker, Hans and Zachow, Stefan and Menzel, Randolf and Rybak, J{\"u}rgen} } @conference {48185, title = {Shape constrained automatic segmentation of the liver based on a heuristic intensity model.}, booktitle = {MICCAI Workshop 3D Segmentation in the Clinic}, year = {2007}, month = {2007 Oct 29}, address = {Brisbane, Australia}, abstract = {

We present a fully automatic 3D segmentation method for the liver from contrast-enhanced CT data. It is based on a combination of a constrained free-form and statistical deformable model. The adap- tation of the model to the image data is performed according to a simple model of the typical intensity distribution around the liver boundary and neighboring anatomical structures, considering the potential presence of tumors in the liver. All parameters of the deformation as well as the initial positioning of the model in the data are estimated automatically.\ 

}, url = {http://mbi.dkfz-heidelberg.de/grand-challenge2007/web/p109.pdf}, author = {Kainmueller, Dagmar and Lange, Thomas and Lamecker, Hans} }