No abstract

Objective. Deep vein thrombosis (DVT) is the third-largest cardiovascular disease, and accurate segmentation of venous thrombus from the black-blood magnetic resonance (MR) images can provide additional information for personalized DVT treatment planning. Therefore, a deep learning network is proposed to automatically segment venous thrombus with high accuracy and reliability. Methods. In order to train, test, and external test the developed network, total images of 110 subjects are obtained from three different centers with two different black-blood MR techniques (i.e., DANTE-SPACE and DANTE-FLASH). Two experienced radiologists manually contoured each venous thrombus, followed by reediting, to create the ground truth. 5-fold cross-validation strategy is applied for training and testing. The segmentation performance is measured on pixel and vessel segment levels. For the pixel level, the dice similarity coefficient (DSC), average Hausdorff distance (AHD), and absolute volume difference (AVD) of segmented thrombus are calculated. For the vessel segment level, the sensitivity (SE), specificity (SP), accuracy (ACC), and positive and negative predictive values (PPV and NPV) are used. Results. The proposed network generates segmentation results in good agreement with the ground truth. Based on the pixel level, the proposed network achieves excellent results on testing and the other two external testing sets, DSC are 0.76, 0.76, and 0.73, AHD (mm) are 4.11, 6.45, and 6.49, and AVD are 0.16, 0.18, and 0.22. On the vessel segment level, SE are 0.95, 0.93, and 0.81, SP are 0.97, 0.92, and 0.97, ACC are 0.96, 0.94, and 0.95, PPV are 0.97, 0.82, and 0.96, and NPV are 0.97, 0.96, and 0.94. Conclusions. The proposed deep learning network is effective and stable for fully automatic segmentation of venous thrombus on black blood MR images.

Introduction: A family history of psychiatric disorders is one of the strongest risk factors for schizophrenia. The characteristics of patients with a family history of psychiatric disorders have not been systematically evaluated.Methods: This multicenter study (26 centers, 2425 cases) was performed in a Chinese population to examine the sociodemographic and clinical characteristics of schizophrenia patients with a family history of psychotic disorders in comparison with those of patients with sporadic schizophrenia.Results: Nineteen percent of patients had a family history of mental disease. Multiple logistic regression analysis revealed that ≥4 hospitalizations (OR = 1.78, P = .004), tobacco dependence (OR = 1.48, P = .006), alcohol dependence (OR = 1.74, P = .013), and physical illness (OR = 1.89, P = .001) were independently and significantly associated with a family history of mental disease. Conclusion:Patients with a family history of mental disorders present different demographics and clinical features than patients without a family history of psychiatric disorders.