This paper proposes a convolutional neural network (CNN) method to estimate subsurface temperature (ST) in the Pacific Ocean from a suite of satellite remote sensing measurements. These include sea surface temperature(SST), sea surface height (SSH), and sea surface salinity (SSS). We propose using the multisource sea surface parameters to establish a monthly CNN model to reconstruct the ocean subsurface temperature (ST) and use Argo data for accurate validation. The results show that the CNN can accurately estimate the ST of the Pacific Ocean by using the model. We trained the model for 12 months. The most prominent months are January, April, July, and October with average mean square error (MSE) values of 0.2659, 0.3129, 0.5318, and 0.5160, and the average coefficients of determination (R 2 ) were 0.968, 0.971, 0.949, and 0.967, respectively. This study improves the accuracy of ST estimation and the good results based on reanalysis indicate that the model is promising to be applied to satellite observations. INDEX TERMS Convolutional neural network, ocean data, satellite measurements, subsurface temperature.
X-ray CT images have been widely used in clinical diagnosis in recent years. A modern CT scanner can generate about 1000 CT slices to show the details of all the human organs within 30 seconds. However, CT image interpretations (viewing 500-1000 slices of CT images manually in front of a screen or films for each patient) require a lot of time and energy. Therefore, computer-aided diagnosis (CAD) systems that can support CT image interpretations are strongly anticipated. Automated recognition of the anatomical structures in CT images is a basic pre-processing of the CAD system. The bone structure is a part of anatomical structures and very useful to act as the landmarks for predictions of the other different organ positions. However, the automated recognition of the bone structure is still a challenging issue. This research proposes an automated scheme for segmenting the bone regions and recognizing the bone structure in noncontrast torso CT images. The proposed scheme was applied to 48 torso CT cases and a subjective evaluation for the experimental results was carried out by an anatomical expert following the anatomical definition. The experimental results showed that the bone structure in 90% CT cases have been recognized correctly. For quantitative evaluation, automated recognition results were compared to manual inputs of bones of lower limb created by an anatomical expert on 10 randomly selected CT cases. The error (maximum distance in 3D) between the recognition results and manual inputs distributed from 3-8 mm in different parts of the bone regions.
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