Statistical shape models are commonly used to analyze the variability between similar anatomical structures and their use is established as a tool for analysis and segmentation of medical images. However, using a global model to capture the variability of complex structures is not enough to achieve the best results. The complexity of a proper global model increases even more when the amount of data available is limited to a small number of datasets. Typically, the anatomical variability between structures is associated to the variability of their physiological regions. In this paper, a complete pipeline is proposed for building a multi-region statistical shape model to study the entire variability from locally identified physiological regions of the inner ear. The proposed model, which is based on an extension of the Point Distribution Model (PDM), is built for a training set of 17 high-resolution images (24.5 μm voxels) of the inner ear. The model is evaluated according to its generalization ability and specificity. The results are compared with the ones of a global model built directly using the standard PDM approach. The evaluation results suggest that better accuracy can be achieved using a regional modeling of the inner ear.
Cochlear implants can restore hearing to deaf or partially deaf patients. In order to plan the intervention, a model from high resolution µCT images is to be built from accurate cochlea segmentations and then, adapted to a patient-specific model. Thus, a precise segmentation is required to build such a model. We propose a new framework for segmentation of µCT cochlear images using random walks where a region term is combined with a distance shape prior weighted by a confidence map to adjust its influence according to the strength of the image contour. Then, the region term can take advantage of the high contrast between the background and foreground and the distance prior guides the segmentation to the exterior of the cochlea as well as to less contrasted regions inside the cochlea. Finally, a refinement is performed preserving the topology using a topological method and an error control map to prevent boundary leakage. We tested the proposed approach with 10 datasets and compared it with the latest techniques with random walks and priors. The experiments suggest that this method gives promising results for cochlea segmentation.
In this study, we quantitatively characterize lung airway remodeling caused by smoking-related emphysema and Chronic
Obstructive Pulmonary Disease (COPD), in low-dose CT scans. To that end, we established three groups of individuals:
subjects with COPD (n=35), subjects with emphysema (n=38) and healthy smokers (n=28). All individuals underwent a
low-dose CT scan, and the images were analyzed as described next. First the lung airways were segmented using a fast
marching method and labeled according to its generation. Along each airway segment, cross-section images were
resampled orthogonal to the airway axis. Next 128 rays were cast from the center of the airway lumen in each crosssection
slice. Finally, we used an integral-based method, to measure lumen radius, wall thickness, mean wall percentage
and mean peak wall attenuation on every cast ray. Our analysis shows that both the mean global wall thickness and the
lumen radius of the airways of both COPD and emphysema groups were significantly different from those of the healthy
group. In addition, the wall thickness change starts at the 3rd airway generation in the COPD patients compared with
emphysema patients, who display the first significant changes starting in the 2nd generation. In conclusion, it is shown
that airway remodeling happens in individuals suffering from either COPD or emphysema, with some local difference
between both groups, and that we are able to detect and accurately quantify this process using images of low-dose CT