@article {48960, title = {Cell dynamics underlying oriented growth of the Drosophila wing imaginal disc.}, journal = {Development (Cambridge, England)}, volume = {144}, year = {2017}, month = {2017 Dec 01}, pages = {4406-4421}, abstract = {

Quantitative analysis of the dynamic cellular mechanisms shaping the Drosophila wing during its larval growth phase has been limited, impeding our ability to understand how morphogen patterns regulate tissue shape. Such analysis requires explants to be imaged under conditions that maintain both growth and patterning, as well as methods to quantify how much cellular behaviors change tissue shape. Here, we demonstrate a key requirement for the steroid hormone 20-hydroxyecdysone (20E) in the maintenance of numerous patterning systems in vivo and in explant culture. We find that low concentrations of 20E support prolonged proliferation in explanted wing discs in the absence of insulin, incidentally providing novel insight into the hormonal regulation of imaginal growth. We use 20E-containing media to observe growth directly and to apply recently developed methods for quantitatively decomposing tissue shape changes into cellular contributions. We discover that whereas cell divisions drive tissue expansion along one axis, their contribution to expansion along the orthogonal axis is cancelled by cell rearrangements and cell shape changes. This finding raises the possibility that anisotropic mechanical constraints contribute to growth orientation in the wing disc.

}, keywords = {Animals, Animals, Genetically Modified, Body Patterning, Cell Proliferation, Drosophila melanogaster, Ecdysterone, Gene Expression Profiling, Gene Expression Regulation, Developmental, Genes, Insect, Imaginal Discs, Insulin, Morphogenesis, Signal Transduction, Transcription, Genetic, Wings, Animal}, issn = {1477-9129}, doi = {10.1242/dev.155069}, author = {Dye, Natalie A and Popovi{\'c}, Marko and Spannl, Stephanie and Etournay, Rapha{\"e}l and Kainm{\"u}ller, Dagmar and Ghosh, Suhrid and Myers, Eugene W and J{\"u}licher, Frank and Eaton, Suzanne} } @article {48172, title = {3D reconstruction of the human rib cage from 2D projection images using a statistical shape model.}, journal = {International journal of computer assisted radiology and surgery}, volume = {5}, year = {2010}, month = {2010 Mar}, pages = {111-24}, abstract = {

PURPOSE: This paper describes an approach for the three-dimensional (3D) shape and pose reconstruction of the human rib cage from few segmented two-dimensional (2D) projection images. Our work is aimed at supporting temporal subtraction techniques of subsequently acquired radiographs by establishing a method for the assessment of pose differences in sequences of chest radiographs of the same patient.

METHODS: The reconstruction method is based on a 3D statistical shape model (SSM) of the rib cage, which is adapted to binary 2D projection images of an individual rib cage. To drive the adaptation we minimize a distance measure that quantifies the dissimilarities between 2D projections of the 3D SSM and the projection images of the individual rib cage. We propose different silhouette-based distance measures and evaluate their suitability for the adaptation of the SSM to the projection images.

RESULTS: An evaluation was performed on 29 sets of biplanar binary images (posterior-anterior and lateral). Depending on the chosen distance measure, our experiments on the combined reconstruction of shape and pose of the rib cages yield reconstruction errors from 2.2 to 4.7 mm average mean 3D surface distance. Given a geometry of an individual rib cage, the rotational errors for the pose reconstruction range from 0.1 degrees to 0.9 degrees.

CONCLUSIONS: The results show that our method is suitable for the estimation of pose differences of the human rib cage in binary projection images. Thus, it is able to provide crucial 3D information for registration during the generation of 2D subtraction images.

}, keywords = {Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Models, Anatomic, Radiographic Image Enhancement, Ribs, Subtraction Technique}, issn = {1861-6429}, doi = {10.1007/s11548-009-0390-2}, author = {Dworzak, Jalda and Lamecker, Hans and von Berg, Jens and Klinder, Tobias and Lorenz, Cristian and Kainm{\"u}ller, Dagmar and Seim, Heiko and Hege, Hans-Christian and Zachow, Stefan} } @article {48171, title = {Comparison and evaluation of methods for liver segmentation from CT datasets.}, journal = {IEEE transactions on medical imaging}, volume = {28}, year = {2009}, month = {2009 Aug}, pages = {1251-65}, abstract = {

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

}, keywords = {Algorithms, Bayes Theorem, Databases, Factual, Humans, Image Processing, Computer-Assisted, Liver, Tomography, X-Ray Computed}, issn = {1558-254X}, doi = {10.1109/TMI.2009.2013851}, author = {Heimann, Tobias and van Ginneken, Bram and Styner, Martin A and Arzhaeva, Yulia and Aurich, Volker and Bauer, Christian and Beck, Andreas and Becker, Christoph and Beichel, Reinhard and Bekes, Gy{\"o}rgy and Bello, Fernando and Binnig, Gerd and Bischof, Horst and Bornik, Alexander and Cashman, Peter M M and Chi, Ying and Cordova, Andr{\'e}s and Dawant, Benoit M and Fidrich, M{\'a}rta and Furst, Jacob D and Furukawa, Daisuke and Grenacher, Lars and Hornegger, Joachim and Kainm{\"u}ller, Dagmar and Kitney, Richard I and Kobatake, Hidefumi and Lamecker, Hans and Lange, Thomas and Lee, Jeongjin and Lennon, Brian and Li, Rui and Li, Senhu and Meinzer, Hans-Peter and Nemeth, G{\'a}bor and Raicu, Daniela S and Rau, Anne-Mareike and van Rikxoort, Eva M and Rousson, Mika{\"e}l and Rusko, L{\'a}szl{\'o} and Saddi, Kinda A and Schmidt, G{\"u}nter and Seghers, Dieter and Shimizu, Akinobu and Slagmolen, Pieter and Sorantin, Erich and Soza, Grzegorz and Susomboon, Ruchaneewan and Waite, Jonathan M and Wimmer, Andreas and Wolf, Ivo} }