Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and point distribution model

Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and a point distribution model is discussed. We present a 2D/3D reconstruction scheme combining statistical extrapolation and regularized shape deformation with an iterative image-to-model correspondence establishing algorithm, and show its application to reconstruct the surface of proximal femur. The image-to-model correspondence is established using a non-rigid 2D point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator and 2D thin-plate splines based deformation to find a fraction of best matched 2D point pairs between features detected from the fluoroscopic images and those extracted from the 3D model. The obtained 2D point pairs are then used to set up a set of 3D point pairs such that we turn a 2D/3D reconstruction problem to a 3D/3D one. We designed and conducted experiments on 11 cadaveric femurs to validate the present reconstruction scheme. An average mean reconstruction error of 1.2 mm was found when two fluoroscopic images were used for each bone. It decreased to 1.0 mm when three fluoroscopic images were used.

Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

Candida species distribution and antifungal susceptibility testing according to European Committee on Antimicrobial Susceptibility Testing and new vs. old Clinical and Laboratory Standards Institute clinical breakpoints: a 6-year prospective candidaemia survey from the fungal infection network of Switzerland

We analyzed the species distribution of Candida blood isolates (CBIs), prospectively collected between 2004 and 2009 within FUNGINOS, and compared their antifungal susceptibility according to clinical breakpoints defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) in 2013, and the Clinical and Laboratory Standards Institute (CLSI) in 2008 (old CLSI breakpoints) and 2012 (new CLSI breakpoints). CBIs were tested for susceptiblity to fluconazole, voriconazole and caspofungin by microtitre broth dilution (Sensititre® YeastOne™ test panel). Of 1090 CBIs, 675 (61.9%) were C. albicans, 191 (17.5%) C. glabrata, 64 (5.9%) C. tropicalis, 59 (5.4%) C. parapsilosis, 33 (3%) C. dubliniensis, 22 (2%) C. krusei and 46 (4.2%) rare Candida species. Independently of the breakpoints applied, C. albicans was almost uniformly (>98%) susceptible to all three antifungal agents. In contrast, the proportions of fluconazole- and voriconazole-susceptible C. tropicalis and F-susceptible C. parapsilosis were lower according to EUCAST/new CLSI breakpoints than to the old CLSI breakpoints. For caspofungin, non-susceptibility occurred mainly in C. krusei (63.3%) and C. glabrata (9.4%). Nine isolates (five C. tropicalis, three C. albicans and one C. parapsilosis) were cross-resistant to azoles according to EUCAST breakpoints, compared with three isolates (two C. albicans and one C. tropicalis) according to new and two (2 C. albicans) according to old CLSI breakpoints. Four species (C. albicans, C. glabrata, C. tropicalis and C. parapsilosis) represented >90% of all CBIs. In vitro resistance to fluconazole, voriconazole and caspofungin was rare among C. albicans, but an increase of non-susceptibile isolates was observed among C. tropicalis/C. parapsilosis for the azoles and C. glabrata/C. krusei for caspofungin according to EUCAST and new CLSI breakpoints compared with old CLSI breakpoints.

Bacteria causing bacteremia in pediatric cancer patients presenting with febrile neutropenia – species distribution and susceptibility patterns

PURPOSE Infections are a major cause of morbidity and mortality in pediatric cancer patients. The aim of this study was to establish the microbiological spectrum and the susceptibility patterns of bacteremia-causing bacteria in pediatric cancer patients with febrile neutropenia in relation to the use of prophylactic and empirical antibiotics. METHODS We analyzed positive blood cultures of pediatric cancer patients presenting with febrile neutropenia between 2004 and 2011 in Groningen and Amsterdam (the Netherlands) and in Bern (Switzerland), using different antibiotic prophylactic and empirical regimens. RESULTS A total of 156 patients with 202 bacteremias, due to 248 bacteria species, were enrolled. The majority (73%) of bacteremias were caused by Gram-positive bacteria. Gram-negative bacteria, especially Pseudomonas aeruginosa, were observed significantly more often in Bern, where no fluoroquinolone prophylaxis was used. Ciprofloxacin-resistant bacteria were cultured more often from patients who did receive ciprofloxacin prophylaxis, compared to the patients who did not (57 versus 11%, p = 0.044). CONCLUSIONS Gram-positive bacteria predominated in this study. We showed that the use of prophylactic antibiotics in pediatric cancer patients was associated with increased resistance rates, which needs further study. The strategy for empiric antimicrobial therapy for febrile neutropenia should be adapted to local antibiotic resistance patterns.

Climate refugia: joint inference from fossil records, species distribution models and phylogeography

Climate refugia, locations where taxa survive periods of regionally adverse climate, are thought to be critical for maintaining biodiversity through the glacial–interglacial climate changes of the Quaternary. A critical research need is to better integrate and reconcile the three major lines of evidence used to infer the existence of past refugia – fossil records, species distribution models and phylogeographic surveys – in order to characterize the complex spatiotemporal trajectories of species and populations in and out of refugia. Here we review the complementary strengths, limitations and new advances for these three approaches. We provide case studies to illustrate their combined application, and point the way towards new opportunities for synthesizing these disparate lines of evidence. Case studies with European beech, Qinghai spruce and Douglas-fir illustrate how the combination of these three approaches successfully resolves complex species histories not attainable from any one approach. Promising new statistical techniques can capitalize on the strengths of each method and provide a robust quantitative reconstruction of species history. Studying past refugia can help identify contemporary refugia and clarify their conservation significance, in particular by elucidating the fine-scale processes and the particular geographic locations that buffer species against rapidly changing climate.

Detection histories for eight species of Amazona parrots in Venezuela during the NeoMaps bird surveys in 2010

Documenting changes in distribution is necessary for understanding species' response to environmental changes, but data on species distributions are heterogeneous in accuracy and resolution. Combining different data sources and methodological approaches can fill gaps in knowledge about the dynamic processes driving changes in species-rich, but data-poor regions. We combined recent bird survey data from the Neotropical Biodiversity Mapping Initiative (NeoMaps) with historical distribution records to estimate potential changes in the distribution of eight species of Amazon parrots in Venezuela. Using environmental covariates and presence-only data from museum collections and the literature, we first used maximum likelihood to fit a species distribution model (SDM) estimating a historical maximum probability of occurrence for each species. We then used recent, NeoMaps survey data to build single-season occupancy models (OM) with the same environmental covariates, as well as with time- and effort-dependent detectability, resulting in estimates of the current probability of occurrence. We finally calculated the disagreement between predictions as a matrix of probability of change in the state of occurrence. Our results suggested negative changes for the only restricted, threatened species, Amazona barbadensis, which has been independently confirmed with field studies. Two of the three remaining widespread species that were detected, Amazona amazonica, Amazona ochrocephala, also had a high probability of negative changes in northern Venezuela, but results were not conclusive for Amazona farinosa. The four remaining species were undetected in recent field surveys; three of these were most probably absent from the survey locations (Amazona autumnalis, Amazona mercenaria and Amazona festiva), while a fourth (Amazona dufresniana) requires more intensive targeted sampling to estimate its current status. Our approach is unique in taking full advantage of available, but limited data, and in detecting a high probability of change even for rare and patchily-distributed species. However, it is presently limited to species meeting the strong assumptions required for maximum-likelihood estimation with presence-only data, including very high detectability and representative sampling of its historical distribution.

A virtual species set for robust and reproducible Species Distribution Modelling tests

Predicting species potential and future distribution has become a relevant tool in biodiversity monitoring and conservation. In this data article we present the suitability map of a virtual species generated based on two bioclimatic variables, and a dataset containing more than 700.000 random observations at the extent of Europe. The dataset includes spatial attributes such as, distance to roads, protected areas, country codes, and the habitat suitability of two spatially clustered species (grassland and forest species) and a wide spread species.