@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 {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 {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 {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} }