In baseline lung cancer CT screening, half of participants with lung nodules have more than one nodule. Lung cancer probability does not significantly change with the number of nodules. Baseline nodule count will not help to differentiate between benign and malignant nodules. Each nodule found in lung cancer screening should be assessed separately independent of the presence of other nodules.
Several geospatial studies and applications require comprehensive semantic information from points-of-interest (POIs). However, this information is frequently dispersed across different collaborative mapping platforms. Surprisingly, there is still a research gap on the conflation of POIs from this type of geo-dataset. In this paper, we focus on the matching aspect of POI data conflation by proposing two matching strategies based on a graph whose nodes represent POIs and edges represent matching possibilities. We demonstrate how the graph is used for (1) dynamically defining the weights of the different POI similarity measures we consider; (2) tackling the issue that POIs should be left unmatched when they do not have a corresponding POI on the other dataset and (3) detecting multiple POIs from the same place in the same dataset and jointly matching these to the corresponding POI(s) from the other dataset. The strategies we propose do not require the collection of training samples or extensive parameter tuning. They were statistically compared with a "naive", though commonly applied, matching approach considering POIs collected from OpenStreetMap and Foursquare from the city of London (England). In our experiments, we sequentially included each of our methodological suggestions in the matching procedure and each of them led to an increase in the accuracy in comparison to the previous results. Our best matching result achieved an overall accuracy of 91%, which is more than 10% higher than the accuracy achieved by the baseline method.
A number of masses arise in relation to the thymus. The radiologist has an important role regarding the differential diagnosis between non-tumoral thymic pathology and malignant thymic tumors. In general, a benign hyperplasia of the thymus occurs in children and young adults, while in adults the thymoma is the most common tumor. Furthermore imaging is of great importance in the preoperative staging and oncological follow-up. To evaluate the thymus CT scan is used in the majority of the cases. MRI or PET-CT can have an added value in the differential diagnosis of various thymic pathologies in some cases. We present an overview of thymic masses with typically imaging features: thymic hyperplasia, thymomas, thymic carcinoma, thymic non-hodgkin lymphoma, thymolipoma and thymic carcinoid.
ObjectivesEarly detection of iron loading is affected by the reproducibility of myocardial contour assessment. A novel semi-automatic myocardial segmentation method is presented on contrast-optimized composite images and compared to the results of manual drawing.Materials and methodsFifty-one short-axis slices at basal, mid-ventricular and apical locations from 17 patients were acquired by bright blood multi-gradient echo MRI. Four observers produced semi-automatic and manual myocardial contours on contrast-optimized composite images. The semi-automatic segmentation method relies on vector field convolution active contours to generate the endocardial contour. After creating radial pixel clusters on the myocardial wall, a combination of pixel-wise coefficient of variance (CoV) assessment and k-means clustering establishes the epicardial contour for each segment.ResultsCompared to manual drawing, semi-automatic myocardial segmentation lowers the variability of T2* quantification within and between observers (CoV of 12.05 vs. 13.86% and 14.43 vs. 16.01%) by improving contour reproducibility (P < 0.001). In the presence of iron loading, semi-automatic segmentation also lowers the T2* variability within and between observers (CoV of 13.14 vs. 15.19% and 15.91 vs. 17.28%).ConclusionApplication of semi-automatic myocardial segmentation on contrast-optimized composite images improves the reproducibility of T2* quantification.
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