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

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

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

Tumor Segmentation in Colorectal Ultrasound Images Using an Ensemble Transfer Learning Model: Towards Intra-Operative Margin Assessment

Tumor boundary identification during colorectal cancer surgery can be challenging, and incomplete tumor removal occurs in approximately 10% of the patients operated for advanced rectal cancer. In this paper, a deep learning framework for automatic tumor segmentation in colorectal ultrasound images was developed, to provide real-time guidance on resection margins using intra-operative ultrasound. A colorectal ultrasound dataset was acquired consisting of 179 images from 74 patients, with ground truth tumor annotations based on histopathology results. To address data scarcity, transfer learning techniques were used to optimize models pre-trained on breast ultrasound data for colorectal ultrasound data. A new custom gradient-based loss function (GWDice) was developed, which emphasizes the clinically relevant top margin of the tumor while training the networks. Lastly, ensemble learning methods were applied to combine tumor segmentation predictions of multiple individual models and further improve the overall tumor segmentation performance. Transfer learning outperformed training from scratch, with an average Dice coefficient over all individual networks of 0.78 compared to 0.68. The new GWDice loss function clearly decreased the average tumor margin prediction error from 1.08 mm to 0.92 mm, without compromising the segmentation of the overall tumor contour. Ensemble learning further improved the Dice coefficient to 0.84 and the tumor margin prediction error to 0.67 mm. Using transfer and ensemble learning strategies, good tumor segmentation performance was achieved despite the relatively small dataset. The developed US segmentation model may contribute to more accurate colorectal tumor resections by providing real-time intra-operative feedback on tumor margins