Context-specific method for detection of soft-tissue lesions in non-cathartic low-dose dual-energy CT colonography

In computed tomographic colonography (CTC), orally administered fecal-tagging agents can be used to indicate residual feces and fluid that could otherwise hide or imitate lesions on CTC images of the colon. Although the use of fecal tagging improves the detection accuracy of CTC, it can introduce image artifacts that may cause lesions that are covered by fecal tagging to have a different visual appearance than those not covered by fecal tagging. This can distort the values of image-based computational features, thereby reducing the accuracy of computer-aided detection (CADe). We developed a context-specific method that performs the detection of lesions separately on lumen regions covered by air and on those covered by fecal tagging, thereby facilitating the optimization of detection parameters separately for these regions and their detected lesion candidates to improve the detection accuracy of CADe. For pilot evaluation, the method was integrated into a dual-energy CADe (DE-CADe) scheme and evaluated by use of leave-one-patient-out evaluation on 66 clinical non-cathartic low-dose dual-energy CTC (DE-CTC) cases that were acquired at a low effective radiation dose and reconstructed by use of iterative image reconstruction. There were 22 colonoscopy-confirmed lesions ≥6 mm in size in 21 patients. The DE-CADe scheme detected 96% of the lesions at a median of 6 FP detections per patient. These preliminary results indicate that the use of context-specific detection can yield high detection accuracy of CADe in non-cathartic low-dose DE-CTC examinations

Individual-Based Modeling and Data Analysis of Ecological Systems Using Machine Learning Techniques

Artificial life (Alife) studies the logic of living systems in an artificial environment in order to gain a deeper insight of the complex processes and governing rules in such systems. EcoSim, an Alife simulation for ecological modeling, is an individual-based predator-prey ecosystem simulation and a generic platform designed to investigate several broad ecological questions, as well as long-term evolutionary patterns and processes in biology and ecology. Speciation and extinction of species are two essential phenomena in evolutionary biology. Many factors are involved in the emergence and disappearance of species. Due to the complexity of the interactions between different factors, such as interaction of individuals with their environment, and the long time required for the observation, studying such phenomena is not easy in the real world. Using data sets obtained from EcoSim and machine learning techniques, we predicted speciation and extinction of species based on numerous factors. Experimental results showed that factors, such as demographics, genetics, and environment are important in the occurrence of these two events in EcoSim.We identified the best species-area relationship (SAR) models, using EcoSim, along with investigating how sampling approaches and sampling scales affect SARs. Further, we proposed a machine learning approach, based on extraction of rules that provide an interpretation of SAR coefficients, to find plausible relationships between the models\u27 coefficients and the spatial information that likely affect SARs. We found the power function family to be a reasonable choice for SAR. Furthermore, the simple power function was the best ranked model in nested sampling amongst models with two coefficients. For some of the SAR model coefficients, we obtained clear correlations with spatial information, thereby providing an interpretation of these coefficients. Rule extraction is a method to discover the rules explaining a predictive model of a specific phenomenon. A procedure for rule extraction from Random Forest (RF) is proposed. The proposed methods are evaluated on eighteen UCI machine learning repository and four microarray data sets. Our experimental results show that the proposed methods outperform one of the state-of-the art methods in terms of scalability and comprehensibility while preserving the same level of accuracy