These results suggest that whole-lesion ADC histogram analysis may facilitate the differentiation between benign and malignant breast mass lesions.

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The accurate detection of satellite components based on optical images can provide data support for aerospace missions such as pointing and tracking between satellites. However, the traditional target detection method is inefficient when performing calculations and has a low detection precision, especially when the attitude of the satellite and illumination conditions change considerably. To enable the precise detection of satellite components, we analyse the imaging characteristics of a satellite in space and propose a method to detect the satellite components. This approach is based on a regional-based convolutional neural network (R-CNN), and it can enable the accurate detection of various satellite components by using optical images. First, on the basis of the Mask R-CNN, we combine the DenseNet, ResNet, and FPN to construct a new feature extraction structure and obtain the R-CNN based satellite-component-detection model (RSD). The feature maps are extracted and concatenated at a deeper multiscale level, and the feature propagation between each layer is enhanced by providing a dense connection. Next, an information-rich satellite dataset is constructed, which is composed of images of various kinds of satellites from various perspectives and orbital positions. The detection model is trained and optimized on the constructed dataset to obtain the satellite component detection model. Finally, the proposed RSD model and original Mask R-CNN are tested on the same established test set. The experimental results show that the proposed detection model has higher precision, recall rate, and F1 score. Therefore, the proposed approach can effectively detect satellite components, based on optical images.

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Background: The apparent diffusion coefficient (ADC) value using histogram analysis is helpful to predict responses to neoadjuvant chemotherapy (NAC) in breast cancer. However, the measurement method has not reached a consensus. This study was to assess the diagnostic performance of the ADC histogram analysis at predicting patient response prior to NAC in breast cancer patients using different region of interest (ROI) selection methods.Methods: A total of 75 patients who underwent diffusion weighted imaging (DWI) prior to NAC were retrospectively enrolled from February 2017 to December 2019. Images were measured using small 2-dimensional (2D) ROI, large 2D ROI, and volume ROI methods. The measurement time and ROI size were recorded. Histopathologic responses were acquired using the Miller-Payne grading system after surgery. The inter-and intra-observer repeatability was analyzed and the ADC histogram values from the three ROI methods were compared. The efficacy of each method at predicting patient response prior to NAC was assessed using the area under the receiver operating characteristic curve (AUC) for the whole study population and subgroups according to molecular subtype.Results: Among the 75 enrolled patients, 26 (34.67%) were responsive to NAC therapy. The ADC histogram values were significantly different among the three ROI methods (P≤0.038). Inter-and intraobserver repeatability of the large 2D ROI method and the volume ROI method was generally greater than that observed with the 2D ROI method. The 10% ADC value of the large 2D ROI method showed the greatest AUC (0.701) in the whole study population and in the luminal subgroup (AUC 0.804). The volume ROI method required significantly more time than the other two ROI methods (P<0.001). Conclusions:The small 2D ROI method is not appropriate for predicting response prior to NAC in breast cancer patients due to the poor repeatability. When choosing the ROI method and the histogram parameters for predicting response prior to NAC in breast cancer patients using ADC-derived histogram analysis, 10% of the large 2D ROI method is recommended, especially in luminal A subtype patients.

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To correct installation errors of star sensors, we propose an error calibration method based on the measurement information from fine guidance sensors (FGS). This work established an extended Kalman filter model by using the measurement information of FGS as the observation to estimate position errors. Simulation experiments are presented to assess the effectiveness of the proposed star sensor calibration technique, and the results demonstrate the improved error estimation accuracy.