A data-assimilated Taiwan Ocean Prediction (ATOP) system is being developed at the National Central University, Taiwan. The model simulates sea-surface height, three-dimensional currents, temperature and salinity and turbulent mixing. The model has options for tracer and particle-tracking algorithms, as well as for wave-induced Stokes drift and wave-enhanced mixing and bottom drag. Two different forecast domains have been tested: a large-grid domain that encompasses the entire North Pacific Ocean at 0.1° × 0.1° horizontal resolution and 41 vertical sigma levels, and a smaller western North Pacific domain which at present also has the same horizontal resolution. In both domains, 25-year spin-up runs from 1988 - 2011 were first conducted, forced by six-hourly Cross-Calibrated Multi-Platform (CCMP) and NCEP reanalysis Global Forecast System (GSF) winds. The results are then used as initial conditions to conduct ocean analyses from January 2012 through February 2012, when updated hindcasts and real-time forecasts begin using the GFS winds. This paper describes the ATOP system and compares the forecast results against satellite altimetry data for assessing model skills. The model results are also shown to compare well with observations of (i) the Kuroshio intrusion in the northern South China Sea, and (ii) subtropical counter current. Review and comparison with other models in the literature of ¡§(i)¡¨ are also given

Precise risk stratification in lymphadenectomy is important for patients with endometrial cancer (EC), to balance the therapeutic benefit against the operation-related morbidity and mortality. We aimed to investigate added values of computer-aided segmentation and machine learning based on clinical parameters and diffusion-weighted imaging radiomics for predicting lymph node (LN) metastasis in EC. This prospective observational study included 236 women with EC (mean age ± standard deviation, 51.2 ± 11.6 years) who underwent magnetic resonance (MR) imaging before surgery during July 2010–July 2018, randomly split into training (n = 165) and test sets (n = 71). A decision-tree model was constructed based on mean apparent diffusion coefficient (ADC) value of the tumor (cutoff, 1.1 × 10⁻³ mm²/s), skewness of the relative ADC value (cutoff, 1.2), short-axis diameter of LN (cutoff, 1.7 mm) and skewness ADC value of the LN (cutoff, 7.2 × 10⁻²), as well as tumor grade (1 vs. 2 and 3), and clinical tumor size (cutoff, 20 mm). The sensitivity and specificity of the model were 94% and 80% for the training set and 86%, 78% for the independent testing set, respectively. The areas under the receiver operating characteristics curve (AUCs) of the decision-tree was 0.85—significantly higher than the mean ADC model (AUC = 0.54) and LN short-axis diameter criteria (AUC = 0.62) (both p < 0.0001). We concluded that a combination of clinical and MR radiomics generates a prediction model for LN metastasis in EC, with diagnostic performance surpassing the conventional ADC and size criteria.

The purpose of this work is to develop a reliable deep-learning-based method that is capable of synthesizing needed CT from MRI for radiotherapy treatment planning. Simultaneously, we try to enhance the resolution of synthetic CT. We adopted pix2pix with a 3D framework, which is a conditional generative adversarial network, to map the MRI data domain into the CT data domain of our dataset. The original dataset contains paired MRI and CT images of 31 subjects; 26 pairs were used for model training and 5 were used for model validation. To identify the correctness of the synthetic CT of models, all of the synthetic CTs were calculated by the quantized image similarity formulas: cosine angle distance, Euclidean distance, mean square error, peak signal-to-noise ratio, and mean structural similarity. Two radiologists independently evaluated the satisfaction score, including spatial, detail, contrast, noise, and artifacts, for each imaging attribute. The mean (±standard deviation) of the structural similarity indices (CAD, L2 norm, MSE, PSNR, and MSSIM) between five real CT scans and the synthetic CT scans were 0.96 ± 0.015, 76.83 ± 12.06, 0.00118 ± 0.00037, 29.47 ± 1.35, and 0.84 ± 0.036, respectively. For synthetic CT, radiologists rated the results as evincing excellent satisfaction in spatial geometry and noise level, good satisfaction in contrast and artifacts, and fair imaging details. The similarity index and clinical evaluation results between synthetic CT and original CT guarantee the usability of the proposed method.