Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images

We present a pulmonary vessel segmentation algorithm, which is fast, fully automatic and robust. It uses a coarse segmentation of the airway tree and a left and right lung labeled volume to restrict a vessel enhancement filter, based on an offset medialness function, to the lungs. We show the application of our algorithm on contrast-enhanced CT images, where we derive a clinical parameter to detect pulmonary hypertension (PH) in patients. Results on a dataset of 24 patients show that quantitative indices derived from the segmentation are applicable to distinguish patients with and without PH. Further work-in-progress results are shown on the VESSEL12 challenge dataset, which is composed of non-contrast-enhanced scans, where we range in the midfield of participating contestants.Comment: Part of the OAGM/AAPR 2013 proceedings (1304.1876

Bone age estimation with the Greulich-Pyle atlas using 3T MR images of hand and wrist.

The age estimation of the hand bones by means of X-ray examination is a pillar of the forensic age estimation. Since the associated radiation exposure is controversial, the search for ionizing radiation-free alternatives such as MRI is part of forensic research. The aim of the current study was to use the Greulich-Pyle (GP) atlas on MR images of the hand and wrist to provide reference values for assessing the age of the hand bones. 3T hand MR images of 238 male participants between the ages of 13 and 21 were acquired using 3D gradient echo sequences (VIBE, DESS). Two readers rated the images using the X-ray-based GP atlas method. A descriptive analysis and a transitional analysis were used for the statistical processing of the data. The agreement between and within the raters was assessed. In addition, a comparison was made with the chronological age and with X-ray studies. The descriptive analysis and the transition analysis showed similar results. Both evaluations showed good agreement with X-ray studies. The comparison with the chronological age showed a difference of 0.37 and 0.54 years for the two readers. The age estimate based on the cross-validated transition analysis showed a mean error of -0.28 years. Inter- and intra-rater agreement were good. In summary, it can be concluded that age estimation of hand bones with MR images is routinely applicable with the GP atlas as an alternative without ionizing radiation. However, in order to reduce the estimation error, a multi-factorial assessment based on examinations of several body regions is still recommended

Automatic segmentation of the left ventricle cavity and myocardium in MRI data

A novel approach for the automatic segmentation has been developed to extract the epi-cardium and endo-cardium boundaries of the left ventricle (lv) of the heart. The developed segmentation scheme takes multi-slice and multi-phase magnetic resonance (MR) images of the heart, transversing the short-axis length from the base to the apex. Each image is taken at one instance in the heart's phase. The images are segmented using a diffusion-based filter followed by an unsupervised clustering technique and the resulting labels are checked to locate the (lv) cavity. From cardiac anatomy, the closest pool of blood to the lv cavity is the right ventricle cavity. The wall between these two blood-pools (interventricular septum) is measured to give an approximate thickness for the myocardium. This value is used when a radial search is performed on a gradient image to find appropriate robust segments of the epi-cardium boundary. The robust edge segments are then joined using a normal spline curve. Experimental results are presented with very encouraging qualitative and quantitative results and a comparison is made against the state-of-the art level-sets method

Age estimation [editorial].

yesAssessing and interpreting dental and skeletal age-related changes in both the living and the dead is of interest to a wide range of disciplines (e.g. see Bittles and Collins 1986) including human biology, paediatrics, public health, palaeodemography, archaeology, palaeontology, human evolution, forensic anthropology and legal medicine. ... This special issue of Annals of Human Biology arises from the 55th annual symposium of the Society for the Study of Human Biology in association with the British Association for Biological Anthropological and Osteoarchaeology held in Oxford, UK, from 9–11 December 2014. Only a selection of the presentations are included here which encompass some of the major recent advances in age estimation from the dentition and skeleton

Three-Dimensional Object Registration Using Wavelet Features

Recent developments in shape-based modeling and data acquisition have brought three-dimensional models to the forefront of computer graphics and visualization research. New data acquisition methods are producing large numbers of models in a variety of fields. Three-dimensional registration (alignment) is key to the useful application of such models in areas from automated surface inspection to cancer detection and surgery. The algorithms developed in this research accomplish automatic registration of three-dimensional voxelized models. We employ features in a wavelet transform domain to accomplish registration. The features are extracted in a multi-resolutional format, thus delineating features at various scales for robust and rapid matching. Registration is achieved by using a voting scheme to select peaks in sets of rotation quaternions, then separately identifying translation. The method is robust to occlusion, clutter, and noise. The efficacy of the algorithm is demonstrated through examples from solid modeling and medical imaging applications

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.Rudyanto, RD.; Kerkstra, S.; Van Rikxoort, EM.; Fetita, C.; Brillet, P.; Lefevre, C.; Xue, W.... (2014). Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Medical Image Analysis. 18(7):1217-1232. doi:10.1016/j.media.2014.07.003S1217123218

Semantic segmentation of colon glands with deep convolutional neural networks and total variation segmentation

Segmentation of histopathology sections is an ubiquitous requirement in digital pathology and due to the large variability of biological tissue, machine learning techniques have shown superior performance over standard image processing methods. As part of the GlaS@MICCAI2015 colon gland segmentation challenge, we present a learning-based algorithm to segment glands in tissue of benign and malignant colorectal cancer. Images are preprocessed according to the Hematoxylin-Eosin staining protocol and two deep convolutional neural networks (CNN) are trained as pixel classifiers. The CNN predictions are then regularized using a figure-ground segmentation based on weighted total variation to produce the final segmentation result. On two test sets, our approach achieves a tissue classification accuracy of 98% and 94%, making use of the inherent capability of our system to distinguish between benign and malignant tissue