LEAPSE: Learning Environment Affordances for 3D Human Pose and Shape Estimation.

We live in a 3D world where people interact with each other in the environment. Learning 3D posed humans therefore requires us to perceive and interpret these interactions. This paper proposes LEAPSE, a novel method that learns salient instance affordances for estimating a posed body from a single RGB image in a non-parametric manner. Existing methods mostly ignore the environment and estimate the human body independently from the surroundings. We capture the influences of non-contact and contact instances on a posed body as an adequate representation of the "environment affordances". The proposed method learns the global relationships between 3D joints, body mesh vertices, and salient instances as environment affordances on the human body. LEAPSE achieved state-of-the-art results on the 3DPW dataset with many affordance instances, and also demonstrated excellent performance on Human3.6M dataset. We further demonstrate the benefit of our method by showing that the performance of existing weak models can be significantly improved when combined with our environment affordance module.

Robust Ship Detection in Infrared Images through Multiscale Feature Extraction and Lightweight CNN.

The sophistication of ship detection technology in remote sensing images is insufficient, the detection results differ substantially from the practical requirements, mainly reflected in the inadequate support for the differentiated application of multi-scene, multi-resolution and multi-type target ships. To overcome these challenges, a ship detection method based on multiscale feature extraction and lightweight CNN is proposed. Firstly, the candidate-region extraction method, based on a multiscale model, can cover the potential targets under different backgrounds accurately. Secondly, the multiple feature fusion method is employed to achieve ship classification, in which, Fourier global spectrum features are applied to discriminate between targets and simple interference, and the targets in complex interference scenarios are further distinguished by using lightweight CNN. Thirdly, the cascade classifier training algorithm and an improved non-maximum suppression method are used to minimise the classification error rate and maximise generalisation, which can achieve final-target confirmation. Experimental results validate our method, showing that it significantly outperforms the available alternatives, reducing the model size by up to 2.17 times while improving detection performance be improved by up to 5.5% in multi-interference scenarios. Furthermore, the robustness ability was verified by three indicators, among which the F-measure score and true-false-positive rate can increase by up to 5.8% and 4.7% respectively, while the mean error rate can decrease by up to 38.2%.

Distributed Optimization of Multiagent Systems Subject to Inequality Constraints.

In this paper, we study a distributed convex optimization problem with inequality constraints. Each agent is associated with its cost function, and can only exchange information with its neighbors. It is assumed that each cost function is convex and the optimization variable is subject to an inequality constraint. The objective is to make all the agents reach consensus, and meanwhile converge to the minimum point of the sum of local cost functions. A distributed protocol is proposed to guarantee that all agents can reach consensus in finite time and converge to the optimal point within the inequality constraints. Based on the ideas of parameter projection, the protocol includes two decent directions. One makes the cost function decrease, and the other makes agents step forward to the constraint set. It is shown that the proposed protocol solves the problem under connected undirected graphs without using a Lagrange multiplier technique. Especially, all of the agents could reach the constraint sets in finite time and stay in there after. The method could also be used in the centralized optimization problems.

Segmentation of coronary arteries images using global feature embedded network with active contour loss.

Coronary heart disease (CHD) is a serious disease that endangers human health and life. In recent years, the morbidity and mortality of CHD are increasing significantly. Because of the particularity and complexity of medical image, it is challenging to segment coronary artery accurately and efficiently. This paper proposes a novel global feature embedded network for better coronary arteries segmentation in 3D coronary computed tomography angiography (CTA) data. The global feature combines multi-level layers from various stages of the network, which contains semantic information and detailed features, aiming to accurately segment target with precise boundary. In addition, we integrate a group of improved noisy activating functions with parameters into our network to eliminate the impact of noise in CTA data. And we improve the learning active contour model, which obtains a refined segmentation result with smooth boundary based on the high-quality score map produced by the networks. The experimental results show that the proposed framework achieved the state-of-the-art performance intuitively and quantitively.

Diagnostic significance of magnetic resonance imaging in patients with cervical cancer after brachytherapy: a meta-analysis.

BACKGROUND: Patients with uterine cervical cancer suffer high mortality. Accurate detection of a residual tumor by magnetic resonance imaging (MRI) during and after directed brachytherapy (BCT) is crucial for the success of cancer treatment and is a significant predictor of patient survival. PURPOSE: To determine the diagnostic significance of MRI in detecting residual tumor tissue after BCT. MATERIAL AND METHODS: The Web of Knowledge, Cochrane Library, and PubMed were systematically searched (January 1997 to December 2016) for post-brachytherapy MRI studies that measured residual tumors in patients with uterine cervical cancer. All data were analyzed using the Meta-Disc 1.4 program. RESULTS: Four clinical studies consisting of 163 patients (147 of whom were included in the present analysis) who were diagnosed with uterine cervical cancer according to the International Federation of Gynecology and Obstetrics (FIGO) staging system were included in the study. All the patients received BCT and underwent MRI detection of residual tumors tissue. In studies where the accuracy of MRI detection was confirmed by histological tests or gynecological tests, the summary estimates of specificity, sensitivity, positive predictive value, negative predictive value, and accuracy were 88.5%, 83.5%, 53.5%, 97.1%, and 84.3%, respectively. CONCLUSION: MRI-directed BCT is commonly used for cervical cancer patients. Based on our investigation of four independent studies, MRI showed better prediction of positive results than negative results in patients with cervical cancer after BCT. However, more data on the greater numbers of patients are needed to establish the accuracy of MRI detection of cervical cancer after BCT.

Value of Diffusion-Weighted Magnetic Resonance Imaging Combined with miR-18a Level in Predicting Radiosensitivity of Cervical Cancer.

BACKGROUND Radioresistance during radiotherapy of cervical cancer often leads to treatment failure; therefore, there is an urgent need to develop effective predictive indicators of radiosensitivity for cervical cancer patients. MATERIAL AND METHODS Cervical cancer cells were collected from 40 patients who received surgical resections. The relationships between apparent diffusion coefficient (ADC) values of masses before surgery and different micro-RNAs (miRNA) levels (miR-18a, miR-132, and miR-145) of these cells were investigated. Cervical cancer cells were divided into 4 groups according to the ADC values of original tumor tissues and expression level of miR-18a. Then, these cells were exposed with irradiation both in vitro and in vivo. RESULTS Advanced cervical cancer showed lower ADC values in magnetic resonance imaging. miR-18a, miR-132, and miR-145 all were increased in the cervical cancer tissues, while miR-18a showed a more marked negative correlation with ADC values. The results of in vitro and in vivo assays showed that higher expression of miR-18a in cervical cancer cells leads to more radiosensitivity, especially in cells from cancer tissues with lower ADC values. CONCLUSIONS The combination of ADC values with expression level of miR-18a may be a new and reliable predictor for radiosensitivity of cervical cancer, helping cervical cancer patients with low ADC values and high expressions of miR-18a to achieve better outcomes in radiotherapy.

Correlation of diffusion tensor imaging parameters and Gleason scores of prostate cancer.

The aim of the present study was to explore the association between the parameters of diffusion tensor imaging (DTI), including fractional anisotropy (FA) values, apparent diffusion coefficient (ADC) values and the diffusion tensor tractography (DTT) map, with the Gleason score of prostate cancer (PCa). A retrospective study of 50 cases of PCa confirmed by biopsy or surgical pathology was performed. Conventional magnetic resonance imaging and DTI scans were conducted in these cases. The 50 cases of PCa were divided into three groups, including low, intermediate and high grade, according to the Gleason score. Post-DTI processing was performed using Neuro 3D software, in order to measure the FA and ADC values, and map the prostate fibers. Differences in FA and ADC values among the various PCa groups were examined using analysis of variance, while the correlation of FA and ADC values with the Gleason score was studied using Pearson correlation analysis. The obtained DTT map clearly demonstrated the spatial structure of the prostate fibers. The fibers of the cancer area were dense without interruption in the low-grade group, sparse and disordered in the intermediate-grade group, and were disordered, sparse or even absent in the high-grade group. The FA values were 0.284±0.313, 0.293±0.347 and 0.369±0.347, respectively, with statistically significant differences observed among the three groups (F=234.533; P<0.05) and between each group (P<0.05). In addition, the FA value of PCa was positively correlated with the Gleason score (r=0.884; P<0.05). The ADC values of the low-, intermediate- and high-grade groups were 1.070±0.072×10-3, 0.961±0.081×10-3 and 0.821±0.048×10-3, respectively, which demonstrated statistically significant differences among the three groups (F=49.987; P<0.05) and between each group (P<0.05). Furthermore, the ADC values of PCa were negatively correlated with Gleason score (r=-0.810; P<0.05). In conclusion, there was an association between DTI parameters and Gleason score, which may be used to evaluate the grading and prognosis of PCa.