UOLO - automatic object detection and segmentation in biomedical images

We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.Comment: Publised on DLMIA 2018. Licensed under the Creative Commons CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0

Nanomaterial-Enhanced Sizings:Design and Optimisation of a Pilot-Scale Fibre Sizing Line

This study focuses on the development of a pilot-scale sizing line, including its initial design and installation, operational phases, and optimization of key process parameters. The primary objective is the identification of critical parameters for achieving a uniform sizing onto the fibres and the determination of optimal conditions for maximum production efficiency. This investigation focused on adjusting the furnace desizing temperature for the removal of commercial sizing, adjusting the drying temperature, as well as optimizing the corresponding residence time of carbon fibres passing through the furnaces. The highest production rate, reaching 1 m sized carbon fibres per minute, was achieved by employing a desizing temperature of 550 °C, a drying temperature of 250 °C, and a residence time of 1 min. Furthermore, a range of sizing solutions was investigated and formulated, exploring carbon-based nanomaterial types with different surface functionalizations and concentrations, to evaluate their impact on the surface morphology and mechanical properties of carbon fibres. In-depth analyses, including scanning electron microscopy and contact angle goniometry, revealed the achievement of a uniform coating on the carbon fibre surface, leading to an enhanced affinity between fibres and the polymeric epoxy matrix. The incorporation of nanomaterials, specifically N2-plasma-functionalized carbon nanotubes and few-layer graphene, demonstrated notable improvements in the interfacial shear properties (90% increase), verified by mechanical and push-out tests

Durability and wear resistance of laser-textured hardened stainless steel surfaces with hydrophobic properties

Hydrophobic surfaces are of high interest to industry. While surface functionalization has attracted significant interest, from both industry and research, the durability of engineered surfaces remains a challenge, as wear and scratches deteriorate their functional response. In this work, a cost-effective combination of surface engineering processes on stainless steel was investigated. Low-temperature plasma surface alloying was applied to increase surface hardness from 172 to 305 HV. Then, near-infrared nanosecond laser patterning was deployed to fabricate channel-like patterns that enabled superhydrophobicity. Abrasion tests were carried out to examine the durability of such engineered surfaces during daily use. In particular, the evolution of surface topographies, chemical composition, and water contact angle with increasing abrasion cycles were studied. Hydrophobicity deteriorated progressively on both hardened and raw stainless steel samples, suggesting that the major contributing factor to hydrophobicity was the surface chemical composition. At the same time, samples with increased surface hardness exhibited a slower deterioration of their topographies when compared with nontreated surfaces. A conclusion is made about the durability of laser-textured hardened stainless steel surfaces produced by applying the proposed combined surface engineering approach

Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe

This ex-vivo study evaluates the feasibility of diffuse reflectance spectroscopy (DRS) for discriminating tumor from healthy tissue, with the aim to develop a technology that can assess resection margins for the presence of tumor cells during oral cavity cancer surgery. Diffuse reflectance spectra were acquired on fresh surgical specimens from 28 patients with oral cavity squamous cell carcinoma. The spectra (400 to 1600 nm) were detected after illuminating tissue with a source fiber at 0.3-, 0.7-, 1.0-, and 2.0-mm distances from a detection fiber, obtaining spectral information from different sampling depths. The spectra were correlated with histopathology. A total of 76 spectra were obtained from tumor tissue and 110 spectra from healthy muscle tissue. The first- A nd second-order derivatives of the spectra were calculated and a classification algorithm was developed using fivefold cross validation with a linear support vector machine. The best results were obtained by the reflectance measured with a 1-mm source-detector distance (sensitivity, specificity, and accuracy are 89%, 82%, and 86%, respectively). DRS can accurately discriminate tumor from healthy tissue in an ex-vivo setting using a 1-mm source-detector distance. Accurate validation methods are warranted for larger sampling depths to allow for guidance during oral cavity cancer excision.</p

Effects of mould wear on hydrophobic polymer surfaces replicated using plasma treated and laser-textured stainless steel inserts

YesThe mass production of polymeric parts with functional surfaces requires economically viable manufacturing routes. Injection moulding is a very attractive option however wear and surface damage can be detrimental to the lifespan of replication masters. In this research, the replication of superhydrophobic surfaces is investigated by employing a process chain that integrates surface hardening, laser texturing and injection moulding. Austenitic stainless steel inserts were hardened by low temperature plasma carburising and three different micro and nano scale surface textures were laser fabricated, i.e. submicron triangular LaserInduced Periodic Surface Structures (LIPSS), micro grooves and Lotus-leaf like topographies. Then, a commonly available talc-loaded polypropylene was used to produce 5000 replicas to investigate the evolution of surface textures on both inserts and replicas together with their functional response. Any wear orsurface damage progressively built up on the inserts during the injection moulding process had a clear impact on surface roughness and peak-to-peak topographies of the replicas. In general, the polymer replicas produced with the carburised inserts retained the wetting properties of their textured surfaces for longer periods compared with those produced with untreated replication masters.European Union’s H2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 675063 (www.laser4fun.eu). The work was also supported by three other H2020 projects, i.e. “HighImpact Injection Moulding Platform for mass-production of 3D and/or large micro-structured surfaces with Antimicrobial, Self-cleaning, Anti-scratch, Anti-squeak and Aesthetic functionalities” (HIMALAIA, No. 766871), “Process Fingerprint for Zero-defect Net-shape Micromanufacturing” (MICROMAN, No. 674801) and “Modular laser based additive manufacturing platform for large scale industrial applications” (MAESTRO, No. 723826). Further support was provided by the UKIERI DST programme “Surface functionalisation for 18/20 Accepted in the journal Tribology – Materials, Surfaces & Interfaces. food, packaging, and healthcare applications

Automatic Retinal Vascularity Identification and Artery/Vein Classification Using Near-Infrared Reflectance Retinographies

The conference was held in Porto, Portugal, February 27 – March 1, 2017.©2019 This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-12209-6_13[Absctract]: The retinal microcirculation structure is commonly used as an important source of information in many medical specialities for the diagnosis of relevant diseases such as, for reference, hypertension, arteriosclerosis, or diabetes. Also, the evaluation of the cerebrovascular and cardiovascular disease progression could be performed through the identification of abnormal signs in the retinal vasculature architecture. Given that these alterations affect differently the artery and vein vascularities, a precise characterization of both blood vessel types is also crucial for the diagnosis and treatment of a significant variety of retinal and systemic pathologies. In this work, we present a fully automatic method for the retinal vessel identification and classification in arteries and veins using Optical Coherence Tomography scans. In our analysis, we used a dataset composed by 30 near-infrared reflectance retinography images from different patients, which were used to test and validate the proposed method. In particular, a total of 597 vessel segments were manually labelled by an expert clinician, being used as groundtruth for the validation process. As result, this methodology achieved a satisfactory performance in the complex issue of the retinal vessel tree identification and classification.This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016-2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04

Discerning natural and anthropogenic organic matter inputs to salt marsh sediments of Ria Formosa lagoon (South Portugal)

Sedimentary organic matter (OM) origin and molecular composition provide useful information to understand carbon cycling in coastal wetlands. Core sediments from threors' Contributionse transects along Ria Formosa lagoon intertidal zone were analysed using analytical pyrolysis (Py-GC/MS) to determine composition, distribution and origin of sedimentary OM. The distribution of alkyl compounds (alkanes, alkanoic acids and alkan-2-ones), polycyclic aromatic hydrocarbons (PAHs), lignin-derived methoxyphenols, linear alkylbenzenes (LABs), steranes and hopanes indicated OM inputs to the intertidal environment from natural-autochthonous and allochthonous-as well as anthropogenic. Several n-alkane geochemical indices used to assess the distribution of main OM sources (terrestrial and marine) in the sediments indicate that algal and aquatic macrophyte derived OM inputs dominated over terrigenous plant sources. The lignin-derived methoxyphenol assemblage, dominated by vinylguaiacol and vinylsyringol derivatives in all sediments, points to large OM contribution from higher plants. The spatial distributions of PAHs (polyaromatic hydrocarbons) showed that most pollution sources were mixed sources including both pyrogenic and petrogenic. Low carbon preference indexes (CPI > 1) for n-alkanes, the presence of UCM (unresolved complex mixture) and the distribution of hopanes (C-29-C-36) and steranes (C-27-C-29) suggested localized petroleum-derived hydrocarbon inputs to the core sediments. Series of LABs were found in most sediment samples also pointing to domestic sewage anthropogenic contributions to the sediment OM.EU Erasmus Mundus Joint Doctorate fellowship (FUECA, University of Cadiz, Spain)EUEuropean Commission [FP7-ENV-2011, 282845, FP7-534 ENV-2012, 308392]MINECO project INTERCARBON [CGL2016-78937-R]info:eu-repo/semantics/publishedVersio

Optic disc segmentation using the sliding band filter,

Background: The optic disc (OD) centre and boundary are important landmarks in retinal images and are essential for automating the calculation of health biomarkers related with some prevalent systemic disorders, such as diabetes, hypertension, cerebrovascular and cardiovascular diseases. Methods: This paper presents an automatic approach for OD segmentation using a multiresolution sliding band filter (SBF). After the preprocessing phase, a low-resolution SBF is applied on a downsampled retinal image and the locations of maximal filter response are used for focusing the analysis on a reduced region of interest (ROI). A high-resolution SBF is applied to obtain a set of pixels associated with the maximum response of the SBF, giving a coarse estimation of the OD boundary, which is regularized using a smoothing algorithm. Results: Our results are compared with manually extracted boundaries from public databases (ONHSD, MESSIDOR and INSPIRE-AVR datasets) outperforming recent approaches for OD segmentation. For the ONHSD, 44% of the results are classified as Excellent, while the remaining images are distributed between the Good (47%) and Fair (9%) categories. An average overlapping area of 83%, 89% and 85% is achieved for the images in ONHSD, MESSIDOR and INSPIR-AVR datasets, respectively, when comparing with the manually delineated OD regions. Discussion: The evaluation results on the images of three datasets demonstrate the better performance of the proposed method compared to recently published OD segmentation approaches and prove the independence of this method when from changes in image characteristics such as size, quality and camera field of view

Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs

The Arteriolar-to-Venular Ratio (AVR) is a popular dimensionless measure which allows the assessment of patients’ condition for the early diagnosis of different diseases, including hypertension and diabetic retinopathy. This paper presents two new approaches for AVR computation in retinal photographs which include a sequence of automated processing steps: vessel segmentation, caliber measurement, optic disc segmentation, artery/vein classification, region of interest delineation, and AVR calculation. Both approaches have been tested on the INSPIRE-AVR dataset, and compared with a ground-truth provided by two medical specialists. The obtained results demonstrate the reliability of the fully automatic approach which provides AVR ratios very similar to at least one of the observers. Furthermore, the semi-automatic approach, which includes the manual modification of the artery/vein classification if needed, allows to significantly reduce the error to a level below the human error