Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

Kernel density correlation based non-rigid point set matching for statistical model-based 2D/3D reconstruction

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images

Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced Computed Tomography (CT) images. Regions of Interest (ROIs) corresponding to the classes: normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (Az) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total Az for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total Az: 0.802+/-0.083) in the discrimination of hepatic tissue.

Sexual selection and speciation: the comparative evidence revisited

The spectacular diversity in sexually selected traits in the animal kingdom has inspired the hypothesis that sexual selection can promote species divergence. In recent years, several studies have attempted to test this idea by correlating species richness with estimates of sexual selection across phylogenies. These studies have yielded mixed results and it remains unclear whether the comparative evidence can be taken as generally supportive. Here, we conduct a meta-analysis of the comparative evidence and find a small but significant positive overall correlation between sexual selection and speciation rate. However, we also find that effect size estimates are influenced by methodological choices. Analyses that included deeper phylogenetic nodes yielded weaker correlations, and different proxies for sexual selection showed different relationships with species richness. We discuss the biological and methodological implications of these findings. We argue that progress requires more representative sampling and justification of chosen proxies for sexual selection and speciation rate, as well as more mechanistic approaches.

The Role of Parasitism in Adaptive Radiations - When Might Parasites Promote and When Might They Constrain Ecological Speciation?

Research on speciation and adaptive radiation has flourished during the past decades, yet factors underlying initiation of reproductive isolation often remain unknown. Parasites represent important selective agents and have received renewed attention in speciation research. We review the literature on parasite-mediated divergent selection in context of ecological speciation and present empirical evidence for three nonexclusive mechanisms by which parasites might facilitate speciation: reduced viability or fecundity of immigrants and hybrids, assortative mating as a pleiotropic by-product of host adaptation, and ecologically-based sexual selection. We emphasise the lack of research on speciation continuums, which is why no study has yet made a convincing case for parasite driven divergent evolution to initiate the emergence of reproductive isolation. We also point interest towards selection imposed by single vs. multiple parasite species, conceptually linking this to strength and multifariousness of selection. Moreover, we discuss how parasites, by manipulating behaviour or impairing sensory abilities of hosts, may change the form of selection that underlies speciation. We conclude that future studies should consider host populations at variable stages of the speciation process, and explore recurrent patterns of parasitism and resistance that could pinpoint the role of parasites in imposing the divergent selection that initiates ecological speciation.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

New results on ν μ → ν τ appearance with the OPERA experiment in the CNGS beam

The OPERA neutrino experiment is designed to perform the first observation of neutrino oscillations in direct appearance mode in the νμ→ντ channel, via the detection of the τ-leptons created in charged current ντ interactions. The detector, located in the underground Gran Sasso Laboratory, consists of an emulsion/lead target with an average mass of about 1.2 kt, complemented by electronic detectors. It is exposed to the CERN Neutrinos to Gran Sasso beam, with a baseline of 730 km and a mean energy of 17 GeV. The observation of the first ντ candidate event and the analysis of the 2008-2009 neutrino sample have been reported in previous publications. This work describes substantial improvements in the analysis and in the evaluation of the detection efficiencies and backgrounds using new simulation tools. The analysis is extended to a sub-sample of 2010 and 2011 data, resulting from an electronic detector-based pre-selection, in which an additional ντ candidate has been observed. The significance of the two events in terms of a νμ→ντ oscillation signal is of 2.40 σ.

Estimation and calibration of the water isotope differential diffusion length in ice core records

Palaeoclimatic information can be retrieved from the diffusion of the stable water isotope signal during firnification of snow. The diffusion length, a measure for the amount of diffusion a layer has experienced, depends on the firn temperature and the accumulation rate. We show that the estimation of the diffusion length using power spectral densities (PSDs) of the record of a single isotope species can be biased by uncertainties in spectral properties of the isotope signal prior to diffusion. By using a second water isotope and calculating the difference in diffusion lengths between the two isotopes, this problem is circumvented. We study the PSD method applied to two isotopes in detail and additionally present a new forward diffusion method for retrieving the differential diffusion length based on the Pearson correlation between the two isotope signals. The two methods are discussed and extensively tested on synthetic data which are generated in a Monte Carlo manner. We show that calibration of the PSD method with this synthetic data is necessary to be able to objectively determine the differential diffusion length. The correlation-based method proves to be a good alternative for the PSD method as it yields precision equal to or somewhat higher than the PSD method. The use of synthetic data also allows us to estimate the accuracy and precision of the two methods and to choose the best sampling strategy to obtain past temperatures with the required precision. In addition to application to synthetic data the two methods are tested on stable-isotope records from the EPICA (European Project for Ice Coring in Antarctica) ice core drilled in Dronning Maud Land, Antarctica, showing that reliable firn temperatures can be reconstructed with a typical uncertainty of 1.5 and 2 °C for the Holocene period and 2 and 2.5 °C for the last glacial period for the correlation and PSD method, respectively.

Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging

Purpose. The purpose of this study was to investigate statistical differences with MR perfusion imaging features that reflect the dynamics of Gadolinium-uptake in MS lesions using dynamic texture parameter analysis (DTPA). Methods. We investigated 51 MS lesions (25 enhancing, 26 nonenhancing lesions) of 12 patients. Enhancing lesions () were prestratified into enhancing lesions with increased permeability (EL+; ) and enhancing lesions with subtle permeability (EL−; ). Histogram-based feature maps were computed from the raw DSC-image time series and the corresponding texture parameters were analyzed during the inflow, outflow, and reperfusion time intervals. Results. Significant differences () were found between EL+ and EL− and between EL+ and nonenhancing inactive lesions (NEL). Main effects between EL+ versus EL− and EL+ versus NEL were observed during reperfusion (mainly in mean and standard deviation (SD): EL+ versus EL− and EL+ versus NEL), while EL− and NEL differed only in their SD during outflow. Conclusion. DTPA allows grading enhancing MS lesions according to their perfusion characteristics. Texture parameters of EL− were similar to NEL, while EL+ differed significantly from EL− and NEL. Dynamic texture analysis may thus be further investigated as noninvasive endogenous marker of lesion formation and restoration.

Computer-based summative assessment of clinical reasoning in clerkships: A mixed-method comparison of key feature problems with case-based multiple choice questions

Background: It is yet unclear if there are differences between using electronic key feature problems (KFPs) or electronic case-based multiple choice questions (cbMCQ) for the assessment of clinical decision making. Summary of Work: Fifth year medical students were exposed to clerkships which ended with a summative exam. Assessment of knowledge per exam was done by 6-9 KFPs, 9-20 cbMCQ and 9-28 MC questions. Each KFP consisted of a case vignette and three key features (KF) using “long menu” as question format. We sought students’ perceptions of the KFPs and cbMCQs in focus groups (n of students=39). Furthermore statistical data of 11 exams (n of students=377) concerning the KFPs and (cb)MCQs were compared. Summary of Results: The analysis of the focus groups resulted in four themes reflecting students’ perceptions of KFPs and their comparison with (cb)MCQ: KFPs were perceived as (i) more realistic, (ii) more difficult, (iii) more motivating for the intense study of clinical reasoning than (cb)MCQ and (iv) showed an overall good acceptance when some preconditions are taken into account. The statistical analysis revealed that there was no difference in difficulty; however KFP showed a higher discrimination and reliability (G-coefficient) even when corrected for testing times. Correlation of the different exam parts was intermediate. Conclusions: Students perceived the KFPs as more motivating for the study of clinical reasoning. Statistically KFPs showed a higher discrimination and higher reliability than cbMCQs. Take-home messages: Including KFPs with long menu questions into summative clerkship exams seems to offer positive educational effects.

Evidence for selection on coloration in a Panamanian poison frog: a coalescent-based approach

Aim The strawberry poison frog, Oophaga pumilio, has undergone a remarkable radiation of colour morphs in the Bocas del Toro archipelago in Panama. This species shows extreme variation in colour and pattern between populations that have been geographically isolated for < 10,000 years. While previous research has suggested the involvement of divergent selection, to date no quantitative test has examined this hypothesis. Location Bocas del Toro archipelago, Panama. Methods We use a combination of population genetics, phylogeography and phenotypic analyses to test for divergent selection in coloration in O. pumilio. Tissue samples of 88 individuals from 15 distinct populations were collected. Using these data, we developed a gene tree using the mitochondrial DNA (mtDNA) d-loop region. Using parameters derived from our mtDNA phylogeny, we predicted the coalescence of a hypothetical nuclear gene underlying coloration. We collected spectral reflectance and body size measurements on 94 individuals from four of the populations and performed a quantitative analysis of phenotypic divergence. Results The mtDNA d-loop tree revealed considerable polyphyly across populations. Coalescent reconstructions of gene trees within population trees revealed incomplete genotypic sorting among populations. The quantitative analysis of phenotypic divergence revealed complete lineage sorting by colour, but not by body size: populations showed non-overlapping variation in spectral reflectance measures of body coloration, while variation in body size did not separate populations. Simulations of the coalescent using parameter values derived from our empirical analyses demonstrated that the level of sorting among populations seen in colour cannot reasonably be attributed to drift. Main conclusions These results imply that divergence in colour, but not body size, is occurring at a faster rate than expected under neutral processes. Our study provides the first quantitative support for the claim that strong diversifying selection underlies colour variation in the strawberry poison frog.