237 research outputs found

    SegReg: Segmenting OARs by Registering MR Images and CT Annotations

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    Organ at risk (OAR) segmentation is a critical process in radiotherapy treatment planning such as head and neck tumors. Nevertheless, in clinical practice, radiation oncologists predominantly perform OAR segmentations manually on CT scans. This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy. Additionally, CT scans offer lower soft-tissue contrast compared to MRI. Despite MRI providing superior soft-tissue visualization, its time-consuming nature makes it infeasible for real-time treatment planning. To address these challenges, we propose a method called SegReg, which utilizes Elastic Symmetric Normalization for registering MRI to perform OAR segmentation. SegReg outperforms the CT-only baseline by 16.78% in mDSC and 18.77% in mIoU, showing that it effectively combines the geometric accuracy of CT with the superior soft-tissue contrast of MRI, making accurate automated OAR segmentation for clinical practice become possible.Comment: Contact: [email protected]

    A Google Earth-based surveillance system for schistosomiasis japonica implemented in the lower reaches of the Yangtze River, China

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    <p>Abstract</p> <p>Background</p> <p>Due to the success of the national schistosomiasis control programme in China, transmission has been sufficiently reduced in many areas to severely limit identification of areas at risk by conventional snail surveys only. In this study, we imported Google Earth technology and a Global Positioning System (GPS) into the monitoring system for schistosomiasis surveillance of the banks of the Yangtze River in Jiangsu Province, China.</p> <p>Methods</p> <p>A total of 45 sites were selected and the risk was assessed monthly by water exposure of sentinel mice at these sites from May to September in 2009 and 2010. The results were assembled and broadcast via the Google Earth platform.</p> <p>Results</p> <p>The intensity of schistosomiasis transmission showed peaks of risk in June and September of 2009, while there was only one small peak in June in 2010 as the number of detected positive transmission sites dropped dramatically that year thanks to improved mollusciciding. River ports were found to be areas of particular risk, but ferry terminals and other centres of river-related activities were also problematic.</p> <p>Conclusions</p> <p>The results confirm that the surveillance system can be rapidly updated and easily maintained, which proves the Google Earth approach to be a user-friendly, inexpensive warning system for schistosomiasis risk.</p

    Some Refinements and Generalizations of I. Schur Type Inequalities

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    Recently, extensive researches on estimating the value of e have been studied. In this paper, the structural characteristics of I. Schur type inequalities are exploited to generalize the corresponding inequalities by variable parameter techniques. Some novel upper and lower bounds for the I. Schur inequality have also been obtained and the upper bounds may be obtained with the help of Maple and automated proving package (Bottema). Numerical examples are employed to demonstrate the reliability of the approximation of these new upper and lower bounds, which improve some known results in the recent literature
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