Objective To evaluate the psychological trauma incurred by the hospitalization survivor of Zhouqu district after the Zhouqu debris flow so as to provide relevant information for psychological and medical interventions. Methods The psychological state of 67 hospitalized survivors of the disaster and other 47 inpatients with similar complaints but not coming from the disaster area was investigated through a mental health self-assessment questionnaire, self-rating anxiety scale (SAS), self-rating depression scale (SDS) and PTSD-SS scale. Results The post-disaster survivors had different levels of psychological problems and post-traumatic stress disorder, and there were significant differences compared to the control group (Plt;0.01, Plt;0.01). The SDS score and the SAS score of the survivor were 48.44+15.648 and 52.92+11.672, respectively, which were all much higher than those of the control group (Plt;0.01, Plt;0.05). Conclusion The debris flow disaster bring serious psychological trauma to the victims. It is necessary to pertinently carry out post-disaster psychological relief including psychological intervention and regulation for the hospitalized survivors, so as to alleviate and reduce their psychological suffering.
Accurate segmentation of pediatric echocardiograms is a challenging task, because significant heart-size changes with age and faster heart rate lead to more blurred boundaries on cardiac ultrasound images compared with adults. To address these problems, a dual decoder network model combining channel attention and scale attention is proposed in this paper. Firstly, an attention-guided decoder with deep supervision strategy is used to obtain attention maps for the ventricular regions. Then, the generated ventricular attention is fed back to multiple layers of the network through skip connections to adjust the feature weights generated by the encoder and highlight the left and right ventricular areas. Finally, a scale attention module and a channel attention module are utilized to enhance the edge features of the left and right ventricles. The experimental results demonstrate that the proposed method in this paper achieves an average Dice coefficient of 90.63% in acquired bilateral ventricular segmentation dataset, which is better than some conventional and state-of-the-art methods in the field of medical image segmentation. More importantly, the method has a more accurate effect in segmenting the edge of the ventricle. The results of this paper can provide a new solution for pediatric echocardiographic bilateral ventricular segmentation and subsequent auxiliary diagnosis of congenital heart disease.