Segmentation of roots in soil with U-Net

Demonstration of the feasibility of a U-Net based CNN system for segmenting images of roots in soil and for replacing the manual line-intersect method

Localise to segment: crop to improve organ at risk segmentation accuracy

Increased organ at risk segmentation accuracy is required to reduce cost and complications for patients receiving radiotherapy treatment. Some deep learning methods for the segmentation of organs at risk use a two stage process where a localisation network first crops an image to the relevant region and then a locally specialised network segments the cropped organ of interest. We investigate the accuracy improvements brought about by such a localisation stage by comparing to a single-stage baseline network trained on full resolution images. We find that localisation approaches can improve both training time and stability and a two stage process involving both a localisation and organ segmentation network provides a significant increase in segmentation accuracy for the spleen, pancreas and heart from the Medical Segmentation Decathlon dataset. We also observe increased benefits of localisation for smaller organs. Source code that recreates the main results is available at \href{https://github.com/Abe404/localise_to_segment}{this https URL}

New Interactive Machine Learning Tool for Marine Image Analysis

We would like to thank the Lofoten Vesterålen Ocean Observatory, and specifically Geir Pedersen,for supplying much of the data used in this study. We would also like to express gratitude to the insightfulcomments made during the review of this manuscript and the efforts of the editorial team during its publication.Peer reviewe

RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy

Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task. We compare the method to the Eclipse contouring software and find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods, with hearts that take 2 minutes and 2 seconds to delineate on average, after 923 images have been delineated, compared to 7 minutes and 1 seconds when delineating manually. Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computer-scientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows. Source code is available at \href{https://github.com/Abe404/RootPainter3D}{this HTTPS URL}

RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation

We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semiautomatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model

Polymerization-based signal amplification for paper-based immunoassays

Diagnostic tests in resource-limited settings require technologies that are affordable and easy to use with minimal infrastructure. Colorimetric detection methods that produce results that are readable by eye, without reliance on specialized and expensive equipment, have great utility in these settings. We report a colorimetric method that integrates a paper-based immunoassay with a rapid, visible-light-induced polymerization to provide high visual contrast between a positive and a negative result. Using Plasmodium falciparum histidine-rich protein 2 as an example, we demonstrate that this method allows visual detection of proteins in complex matrices such as human serum and provides quantitative information regarding analyte levels when combined with cellphone-based imaging. It also allows the user to decouple the capture of analyte from signal amplification and visualization steps.Bill & Melinda Gates Foundation (Award 51308)United States. Defense Advanced Research Projects Agency (HR0011-12-2-0010)National Science Foundation (U.S.). Graduate Research FellowshipBurroughs Wellcome Fund (Career Award at the Scientific Interface

Prediction of post-radiotherapy recurrence volumes in head and neck squamous cell carcinoma using 3D U-Net segmentation

Locoregional recurrences (LRR) are still a frequent site of treatment failure for head and neck squamous cell carcinoma (HNSCC) patients. Identification of high risk subvolumes based on pretreatment imaging is key to biologically targeted radiation therapy. We investigated the extent to which a Convolutional neural network (CNN) is able to predict LRR volumes based on pre-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) scans in HNSCC patients and thus the potential to identify biological high risk volumes using CNNs. For 37 patients who had undergone primary radiotherapy for oropharyngeal squamous cell carcinoma, five oncologists contoured the relapse volumes on recurrence CT scans. Datasets of pre-treatment FDG-PET/CT, gross tumour volume (GTV) and contoured relapse for each of the patients were randomly divided into training (n=23), validation (n=7) and test (n=7) datasets. We compared a CNN trained from scratch, a pre-trained CNN, a SUVmax threshold approach, and using the GTV directly. The SUVmax threshold method included 5 out of the 7 relapse origin points within a volume of median 4.6 cubic centimetres (cc). Both the GTV contour and best CNN segmentations included the relapse origin 6 out of 7 times with median volumes of 28 and 18 cc respectively. The CNN included the same or greater number of relapse volume POs, with significantly smaller relapse volumes. Our novel findings indicate that CNNs may predict LRR, yet further work on dataset development is required to attain clinically useful prediction accuracy

Contextualizing the Global Nursing Care Chain: International Migration and the Status of Nursing in Kerala, India

In this article I explore the issue of nursing status in Kerala, India and how over time a colonial discourse of caste‐based pollution has given way to a discourse of sexual pollution under expanding migratory opportunities. Based on survey and qualitative research findings, I caution that the improving occupational status of nursing in India is not directly mapped onto social status, and this is particularly evident in the matrimonial market. In the light of these findings I argue that global nursing care chain (GNCC) analysis must assess more than just workplace contexts in order to conceptualize how global care chains (GCCs) interlock, and how they are differentiated from each other

Inhibition of HIV replication by amino-sugar derivatives

AbstractThe plant alkaloids castanospermine, dihydroxymethyldihydroxypyrrolidine and deoxynojirimycin have recently been shown to have potential anti-HIV activity [(1987) Proc. Natl. Acad. Sci. USA 84, 8120–8124; (1987) Nature 330, 74–77; (1987) Lancet i, 1025–1026]. They are thought to act by inhibiting α-glucosidase I, an enzyme involved in the processing of N-linked oligosaccharides on glycoproteins. We report here the relative efficacy of a spectrum of amino-sugar derivatives as inhibition of HIV cytopathicity. Several α-glucosidase inhibitors and α-fucosidase inhibitors were found to be active at concentrations which were non-cytotoxic

Perceived Threat, Risk Perception, and Efficacy Beliefs Related to SARS and Other (Emerging) Infectious Diseases: Results of an International Survey

PURPOSE: To study the levels of perceived threat, perceived severity, perceived vulnerability, response efficacy, and self-efficacy for severe acute respiratory syndrome (SARS) and eight other diseases in five European and three Asian countries. METHOD: A computer-assisted phone survey was conducted among 3,436 respondents. The questionnaire focused on perceived threat, vulnerability, severity, response efficacy, and self-efficacy related to SARS and eight other diseases. RESULTS: Perceived threat of SARS in case of an outbreak in the country was higher than that of other diseases. Perceived vulnerability of SARS was at an intermediate level and perceived severity was high compared to other diseases. Perceived threat for SARS varied between countries in Europe and Asia with a higher perceived severity of SARS in Europe and a higher perceived vulnerability in Asia. Response efficacy and self-efficacy for SARS were higher in Asia compared to Europe. In multiple linear regression analyses, country was strongly associated with perceived threat. CONCLUSIONS: The relatively high perceived threat for SARS indicates that it is seen as a public health risk and offers a basis for communication in case of an outbreak. The strong association between perceived threat and country and different regional patterns require further researc