Determination of Pelvic Orientation from Ultrasound Images Using Patch-SSMs and a Hierarchical Speed of Sound Compensation Strategy

In the field of computer assisted orthopedic surgery (CAOS) the anterior pelvic plane (APP) is a common concept to determine the pelvic orientation by digitizing distinct pelvic landmarks. As percutaneous palpation is - especially for obese patients - known to be error-prone, B-mode ultrasound (US) imaging could provide an alternative means. Several concepts of using ultrasound imaging to determine the APP landmarks have been introduced. In this paper we present a novel technique, which uses local patch statistical shape models (SSMs) and a hierarchical speed of sound compensation strategy for an accurate determination of the APP. These patches are independently matched and instantiated with respect to associated point clouds derived from the acquired ultrasound images. Potential inaccuracies due to the assumption of a constant speed of sound are compensated by an extended reconstruction scheme. We validated our method with in-vitro studies using a plastic bone covered with a soft-tissue simulation phantom and with a preliminary cadaver trial.

A conceptual model of compensation/decompensation in lumbar segmental instability

Lumbar spinal instability (LSI) is a common spinal disorder and can be associated with substantial disability. The concept of defining clinically relevant classifications of disease or 'target condition' is used in diagnostic research. Applying this concept to LSI we hypothesize that a set of clinical and radiological criteria can be developed to identify patients with this target condition who are at high risk of 'irreversible' decompensated LSI for whom surgery becomes the treatment of choice. In LSI, structural deterioration of the lumbar disc initiates a degenerative cascade of segmental instability. Over time, radiographic signs become visible: traction spurs, facet joint degeneration, misalignment, stenosis, olisthesis and de novo scoliosis. Ligaments, joint capsules, local and distant musculature are the functional elements of the lumbar motion segment. Influenced by non-functional factors, these functional elements allow a compensation of degeneration of the motion segment. Compensation may happen on each step of the degenerative cascade but cannot reverse it. However, compensation of LSI may lead to an alleviation or resolution of clinical symptoms. In return, the target condition of decompensation of LSI may cause the new occurrence of symptoms and pain. Functional compensation and decompensation are subject to numerous factors that can change which makes estimation of an individual's long-term prognosis difficult. Compensation and decompensation may influence radiographic signs of degeneration, e.g. the degree of misalignment and segmental angulation caused by LSI is influenced by the tonus of the local musculature. This conceptual model of compensation/decompensation may help solve the debate on functional and psychosocial factors that influence low back pain and to establish a new definition of non-specific low back pain. Individual differences of identical structural disorders could be explained by compensated or decompensated LSI leading to changes in clinical symptoms and pain. Future spine surgery will have to carefully define and measure functional aspects of LSI, e.g. to identify a point of no return where multidisciplinary interventions do not allow a re-compensation and surgery becomes the treatment of choice.

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.