Reconstruction of 3D Vertebral Models from a Single 2D Lateral Fluoroscopic Image

Accurate three-dimensional (3D) models of lumbar vertebrae are required for image-based 3D kinematics analysis. MRI or CT datasets are frequently used to derive 3D models but have the disadvantages that they are expensive, time-consuming or involving ionizing radiation (e.g., CT acquisition). In this chapter, we present an alternative technique that can reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image and a statistical shape model. Cadaveric studies are conducted to verify the reconstruction accuracy by comparing the surface models reconstructed from a single lateral fluoroscopic image to the ground truth data from 3D CT segmentation. A mean reconstruction error between 0.7 and 1.4 mm was found.

Graphical Model-Based Vertebra Identification from X-Ray Image(s)

Automated identification of vertebrae from X-ray image(s) is an important step for various medical image computing tasks such as 2D/3D rigid and non-rigid registration. In this chapter we present a graphical model-based solution for automated vertebra identification from X-ray image(s). Our solution does not ask for a training process using training data and has the capability to automatically determine the number of vertebrae visible in the image(s). This is achieved by combining a graphical model-based maximum a posterior probability (MAP) estimate with a mean-shift based clustering. Experiments conducted on simulated X-ray images as well as on a low-dose low quality X-ray spinal image of a scoliotic patient verified its performance.

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.

Morphological alterations in the liver of yellow perch (Perca flavescens) from a biological mercury hotspot

Mercury (Hg) contamination is a global issue due to its anthropogenic release, long-range transport, and deposition in remote areas. In Kejimkujik National Park and National Historic Site, Nova Scotia, Canada, high concentrations of total mercury (THg) were found in tissues of yellow perch (Perca flavescens). The aim of this study was to evaluate a possible relationship between THg concentrations and the morphology of perch liver as a main site of metal storage and toxicity. Yellow perch were sampled from five lakes known to contain fish representing a wide range in Hg concentrations in fall 2013. The ultrastructure of hepatocytes and the distribution of Hg within the liver parenchyma were analyzed by transmission electron microscopy (TEM) and electron energy loss spectrometry (EELS). The relative area of macrophage aggregates (MAs) in the liver was determined using image analysis software and fluorescence microscopy. No relation between general health indicators (Fulton's condition index) and THg was observed. In line with this, TEM examination of the liver ultrastructure revealed no prominent pathologies related to THg accumulation. However, a morphological parameter that appeared to increase with muscle THg was the relative area of MAs in the liver. The hepatic lysosomes appeared to be enlarged in samples with the highest THg concentrations. Interestingly, EELS analysis revealed that the MAs and hepatic lysosomes contained Hg.

Medical image computing in diagnosis and intervention of spinal diseases

Spinal image analysis and computer assisted intervention have emerged as new and independent research areas, due to the importance of treatment of spinal diseases, increasing availability of spinal imaging, and advances in analytics and navigation tools. Among others, multiple modality spinal image analysis and spinal navigation tools have emerged as two keys in this new area. We believe that further focused research in these two areas will lead to a much more efficient and accelerated research path, avoiding detours that exist in other applications, such as in brain and heart.