@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} } @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 {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 {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} }