Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements

In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements from all patches together in a data driven way, by considering not only the training data but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.

FACTS: Fully Automatic CT Segmentation of a Hip Joint

Extraction of surface models of a hip joint from CT data is a pre-requisite step for computer assisted diagnosis and planning (CADP) of periacetabular osteotomy (PAO). Most of existing CADP systems are based on manual segmentation, which is time-consuming and hard to achieve reproducible results. In this paper, we present a Fully Automatic CT Segmentation (FACTS) approach to simultaneously extract both pelvic and femoral models. Our approach works by combining fast random forest (RF) regression based landmark detection, multi-atlas based segmentation, with articulated statistical shape model (aSSM) based fitting. The two fundamental contributions of our approach are: (1) an improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the multi-atlas based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 6-fold cross validation. When the present approach was compared to manual segmentation, a mean segmentation accuracy of 0.40, 0.36, and 0.36 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. When the models derived from both segmentations were used to compute the PAO diagnosis parameters, a difference of 2.0 ± 1.5°, 2.1 ± 1.6°, and 3.5 ± 2.3% were found for anteversion, inclination, and acetabular coverage, respectively. The achieved accuracy is regarded as clinically accurate enough for our target applications.

Management of Joint Research Activities within ERA-ARD: Guidelines for ARD programme planning, monitoring, evaluation and impact assessment

The present document has been elaborated in the context of the ERA-ARD project “The Agricultural Research for Development (ARD) dimension of the European Research Area (ERA) “. It is based on work done within Task 3.2 to identify a set of common or compatible methodologies for ARD planning, monitoring and evaluation and impact assessment. This set should serve as a guide for the management of joint ARD activities that are presently developed within the framework of the ERA-ARD project.

Biomechanical properties of the atlantoaxial joint with naturally-occurring instability in a toy breed dog. A comparative descriptive cadaveric study

The biomechanical properties of the atlanto-axial joint in a young Yorkshire Terrier dog with spontaneous atlantoaxial instability were compared to those of another young toy breed dog with a healthy atlantoaxial joint. The range-of-motion was increased in flexion and lateral bending in the unstable joint. In addition, lateral bending led to torsion and dorsal dislocation of the axis within the atlas. On gross examination, the dens ligaments were absent and a longitudinal tear of the tectorial membrane was observed. These findings suggest that both ventral and lateral flexion may lead to severe spinal cord compression, and that the tectorial membrane may play a protective role in some cases of atlantoaxial instability.