Implicit deformable models for biomedical image segmentation.

In this thesis, new methods for the efficient segmentation of images are presented. The proposed methods are based on the deformable model approach, and can be used efficiently in the segmentation of complex geometries from various imaging modalities. A novel deformable model that is based on a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions is presented. This external force field is based on hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contributes to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and give the deformable model a high invariance in initialization configurations. The voxel interactions across the whole image domain provides a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force held allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, it is shown that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. A comparative study on the segmentation of various geometries with different topologies from both synthetic and real images is provided, and the proposed method is shown to achieve significant improvements against several existing techniques. A robust framework for the segmentation of vascular geometries is described. In particular, the framework consists of image denoising, optimal object edge representation, and segmentation using implicit deformable model. The image denoising is based on vessel enhancing diffusion which can be used to smooth out image noise and enhance the vessel structures. The image object boundaries are derived using an edge detection technique which can produce object edges of single pixel width. The image edge information is then used to derive the geometric interaction field for optimal object edge representation. The vascular geometries are segmented using an implict deformable model. A region constraint is added to the deformable model which allows it to easily get around calcified regions and propagate across the vessels to segment the structures efficiently. The presented framework is ai)plied in the accurate segmentation of carotid geometries from medical images. A new segmentation model with statistical shape prior using a variational approach is also presented in this thesis. The proposed model consists of an image attraction force that propagates contours towards image object boundaries, and a global shape force that attracts the model towards similar shapes in the statistical shape distribution. The image attraction force is derived from gradient vector interactions across the whole image domain, which makes the model more robust to image noise, weak edges and initializations. The statistical shape information is incorporated using kernel density estimation, which allows the shape prior model to handle arbitrary shape variations. It is shown that the proposed model with shape prior can be used to segment object shapes from images efficiently

Enhancing CO2 Electroreduction to Ethanol on Copper-Silver Composites by Opening an Alternative Catalytic Pathway

A fundamental question in the electrochemical CO2 reduction reaction (CO2RR) is how to rationally control the catalytic selectivity. For instance, adding a CO-producing metal like Ag to Cu shifts the latter’s CO2RR selectivity towards C2 products, but the underlying cause of the change is unclear. Herein, we show that CuAg boundaries facilitate the coupling of carbon-containing species to give ethanol, through an otherwise closed pathway. Oxide-derived Cu nanowires mixed with 20 nm Ag particles (Cu:Ag mole ratio of 1:20) reduce CO2 to ethanol with a current density of -4.1 mA/cm2 at -1.1 V vs. RHE and ethanol/ethylene Faradaic efficiency ratio of 1.1. These figures of merit are respectively 5 and 3 times higher than those for pure oxide-derived Cu nanowires. CO2RR using different Ag:Cu ratios and Ag particle sizes reveals that ethanol production scales with CO production on the Ag sites and the abundance of CuAg boundaries, and, very interestingly, without significant modifications to ethylene formation. Computational modelling shows selective ethanol evolution via Langmuir-Hinshelwood *CO + *CHx (x = 1, 2) coupling at CuAg boundaries, and that the formation of energy-intensive CO dimers is circumvented.This work is supported by an academic research fund (R-143-000-683-112) from the Ministry of Education, Singapore and the National University of Singapore Flagship Green Energy Program (R-143-000-A55-646 and R-143-000-A55-733). F.C.-V acknowledges funding from Spanish MICIUN RTI2018-095460–B-I00 and María de Maeztu MDM-2017-0767 grants and, in part, by Generalitat de Catalunya 2017SGR13. O.P. thanks the Spanish MICIUN for a PhD grant (PRE2018-083811). We thank Red Española de Supercomputación (RES) for supercomputing time at SCAYLE (projects QS-2019-3-0018, QS-2019-2-0023 and QCM-2019-1-0034). The use of supercomputing facilities at SURFsara was sponsored by NWO Physical Sciences, with financial support by NWO. We also thank Cheryldine Lim from the SERIS for assisting with SEM and EDX mapping experiments, and Futian You and Ka Yau Lee from the NUS for assisting with TEM imaging

A collaboration platform for enabling industrial symbiosis : towards creating a self-learning waste-to-resource database for recommending industrial symbiosis transactions using text analytics

Industrial Symbiosis (IS) adopts a collaborative approach, which aims to re-channel resources – traditionally considered spent and non-productive – towards alternative value-adding pathways. Empirically, the concept of IS has been rapidly implemented in practice through a facilitated approach, whereby businesses are engaged and “match-made” via a facilitating body. While recommending alternative pathways for companies to establish IS-based transactions is a long-standing practice, recent technological advancement has shifted the nature of this task from one that is based purely on human intellect and reasoning, towards one which leverages intelligent recommendation algorithms to provide relevant suggestions. Traditionally, these recommendation engines rely on manually populated knowledge bases that are not only labor-intensive to build but also costly to maintain. This work presents the creation of a self-learning waste-to-resource database supporting an IS recommendation system by utilizing text analytics techniques. We further demonstrate its practical application to support IS facilitating bodies in their core activity

Anesthesia of a dental patient with Angelman syndrome -A case report-

Angelman syndrome is characterized by a partial deficit of paired autosomal chromosome 15, which contains a subunit of the GABA (Gamma-Amino Butyric Acid) receptor. Many drugs that act on the CNS (Central Nerve System) during anesthesia are believed to exert their effects via the GABA receptors. We describe the anesthesia of a 7 year-old female patient with Angelman syndrome who underwent surgery for dental caries. The basic factors that needed to be considered when administering anesthesia to this patient were epilepsy, significant dominance of the vagal tone, craniofacial abnormalities and peripheral muscular atrophy. Inhalational anesthetics (sevoflurane) were employed for this patient. The patient had an uneventful peri-operative period and was discharged home on the same day of the operation

Message from the ICASE 2011 organizers

published_or_final_versionThe 9th IEEE International Symposium on Parallel and Distributed Processing with Applications Workshops (ISPAW 2011), Busan, Korea, 26-28 May 2011. In Proceedings of the ISPAW, 2011, p. xxx

Multiplex PCR System for Rapid Detection of Pathogens in Patients with Presumed Sepsis – A Systemic Review and Meta-Analysis

Background: Blood culture is viewed as the golden standard for the diagnosis of sepsis but suffers from low sensitivity and long turnaround time. LightCycler SeptiFast (LC-SF) is a real-time multiplex polymerase chain reaction test able to detect 25 common pathogens responsible for bloodstream infections within hours. We aim to assess the accuracy of LC-SF by systematically reviewing the published studies. Method Related literature on Medline, Embase, and Cochrane databases was searched up to October 2012 for studies utilizing LC-SF to diagnose suspected sepsis and that provided sufficient data to construct two-by-two tables. Results: A total of 34 studies enrolling 6012 patients of suspected sepsis were included. The overall sensitivity and specificity for LC-SF to detect bacteremia or fungemia was 0·75 (95% CI: 0·65–0·83) and 0·92 (95%CI:0·90–0·95), respectively. LC-SF had a high positive likelihood ratio (10·10) and a moderate negative likelihood ratio (0·27). Specifically, LC-SF had a sensitivity of 0·80 (95%CI: 0·70–0·88) and a specificity of 0·95(95%CI: 0·93–0·97) for the bacteremia outcome, and a sensitivity of 0·61 (95%CI: 0·48–0·72) and a specificity of 0·99 (95%CI: 0·99–0·99) for the fungemia outcome. High heterogeneity was found in the bacteremia outcome subgroup but not in the fungemia outcome subgroup. Conclusion: LC-SF is of high rule-in value for early detection of septic patients. In a population with low pretest probability, LC-SF test can still provide valuable information for ruling out bacteremia or fungemia

A Large Bandgap Shift in InGaAs(P)/InP Multi-Quantum Well Structure Obtained by Impurity-Free Vacancy Diffusion Using SiO2 Capping and its Application to Photodetectors

In this paper, we have investigated the bandgap tuning in the InGaAs (P)/ InP multiquantum well (MQW) structure obtained by impurity-free vacancy diffusion (IFVD) using low temperature photoluminescence (PL). The MQW intermixing was performed in a rapid thermal annealer (RTA) using the dielectric capping materials, Si02 and SiNX. The Si02 capping was successfully used with InGaAs cap layer to cause a large bandgap tuning effect in the InGaAs/InP MQW material. The blue shift of bandgap energy after RTA treatment was as much as 185 and 230 meV at 750 t and 850 t, respectively, with its value controllable using annealing time and temperature. Samples with Si02-InP or SiN-InGaAs cap layer combinations, on the other hand, did not show any significant energy shifts. The absorption spectra taken from the same samples confimed the energy shifts obtained using PL. The process developed can be readily applied to fabrication of photodetectors that are sensitive to wavelength and/or polarization.This work was fmancially supported in part by OERC(Opto-Electronic Research Center) through the grant # 97K3-0809- 02-06-1 and by the SPRC (Semiconductor Physics Research Center) of Korea. The authors thank U. H. Lee and Prof. D. Lee of Chung Nam National Univ. for their help with the absorption measurement