In the last issue,two case reports separately present examples of the extremely rare and complex congenital heart diseases that show concordant atrioventricular connections to the L-looped ventricles in the presence o...In the last issue,two case reports separately present examples of the extremely rare and complex congenital heart diseases that show concordant atrioventricular connections to the L-looped ventricles in the presence of situs solitus.Both cases highlight that the relationship between the two ventricles within the ventricular mass is not always harmonious with the given atrioventricular connection.Such disharmony between the connections and relationships requires careful assessment of the three basic facets of cardiac building blocks,namely their morphology,the relationship of their component parts,and their connections with the adjacent segments.3D imaging and printing can now facilitate an otherwise difficult diagnosis in such complex situations.Rotation of either the 3D images or the models permit accurate assessment of the ventricular topologic pattern by creating the right ventricular en-face septal view,thus facilitating placement of the observer’s hands.As we now emphasize,an alternative approach,which might prove more attractive to imagers,is to rotate the ventricular mass to provide the ventricular apical view,thus permitting determination of the ventricular relationship without using the hands.展开更多
The complex relationship between structural connectivity(SC) and functional connectivity(FC) of human brain networks is still a critical problem in neuroscience. In order to investigate the role of SC in shaping resti...The complex relationship between structural connectivity(SC) and functional connectivity(FC) of human brain networks is still a critical problem in neuroscience. In order to investigate the role of SC in shaping resting-state FC, numerous models have been proposed. Here, we use a simple dynamic model based on the susceptible-infected-susceptible(SIS) model along the shortest paths to predict FC from SC. Unlike the previous dynamic model based on SIS theory, we focus on the shortest paths as the principal routes to transmit signals rather than the empirical structural brain network. We first simplify the structurally connected network into an efficient propagation network according to the shortest paths and then combine SIS infection theory with the efficient network to simulate the dynamic process of human brain activity. Finally, we perform an extensive comparison study between the dynamic models embedded in the efficient network, the dynamic model embedded in the structurally connected network and dynamic mean field(DMF) model predicting FC from SC. Extensive experiments on two different resolution datasets indicate that i) the dynamic model simulated on the shortest paths can predict FC among both structurally connected and unconnected node pairs; ii) though there are fewer links in the efficient propagation network, the predictive power of FC derived from the efficient propagation network is better than the dynamic model simulated on a structural brain network; iii) in comparison with the DMF model,the dynamic model embedded in the shortest paths is found to perform better to predict FC.展开更多
Computed Tomography(CT)is a commonly used technology in Printed Circuit Boards(PCB)non-destructive testing,and element segmentation of CT images is a key subsequent step.With the development of deep learning,researche...Computed Tomography(CT)is a commonly used technology in Printed Circuit Boards(PCB)non-destructive testing,and element segmentation of CT images is a key subsequent step.With the development of deep learning,researchers began to exploit the“pre-training and fine-tuning”training process for multi-element segmentation,reducing the time spent on manual annotation.However,the existing element segmentation model only focuses on the overall accuracy at the pixel level,ignoring whether the element connectivity relationship can be correctly identified.To this end,this paper proposes a PCB CT image element segmentation model optimizing the semantic perception of connectivity relationship(OSPC-seg).The overall training process adopts a“pre-training and fine-tuning”training process.A loss function that optimizes the semantic perception of circuit connectivity relationship(OSPC Loss)is designed from the aspect of alleviating the class imbalance problem and improving the correct connectivity rate.Also,the correct connectivity rate index(CCR)is proposed to evaluate the model’s connectivity relationship recognition capabilities.Experiments show that mIoU and CCR of OSPC-seg on our datasets are 90.1%and 97.0%,improved by 1.5%and 1.6%respectively compared with the baseline model.From visualization results,it can be seen that the segmentation performance of connection positions is significantly improved,which also demonstrates the effectiveness of OSPC-seg.展开更多
文摘In the last issue,two case reports separately present examples of the extremely rare and complex congenital heart diseases that show concordant atrioventricular connections to the L-looped ventricles in the presence of situs solitus.Both cases highlight that the relationship between the two ventricles within the ventricular mass is not always harmonious with the given atrioventricular connection.Such disharmony between the connections and relationships requires careful assessment of the three basic facets of cardiac building blocks,namely their morphology,the relationship of their component parts,and their connections with the adjacent segments.3D imaging and printing can now facilitate an otherwise difficult diagnosis in such complex situations.Rotation of either the 3D images or the models permit accurate assessment of the ventricular topologic pattern by creating the right ventricular en-face septal view,thus facilitating placement of the observer’s hands.As we now emphasize,an alternative approach,which might prove more attractive to imagers,is to rotate the ventricular mass to provide the ventricular apical view,thus permitting determination of the ventricular relationship without using the hands.
基金supported by China Scholarship Council(201306455001)the National Natural Science Foundation of China(61271407)the Fundamental Research Funds for the Central Universities(16CX06050A)
文摘The complex relationship between structural connectivity(SC) and functional connectivity(FC) of human brain networks is still a critical problem in neuroscience. In order to investigate the role of SC in shaping resting-state FC, numerous models have been proposed. Here, we use a simple dynamic model based on the susceptible-infected-susceptible(SIS) model along the shortest paths to predict FC from SC. Unlike the previous dynamic model based on SIS theory, we focus on the shortest paths as the principal routes to transmit signals rather than the empirical structural brain network. We first simplify the structurally connected network into an efficient propagation network according to the shortest paths and then combine SIS infection theory with the efficient network to simulate the dynamic process of human brain activity. Finally, we perform an extensive comparison study between the dynamic models embedded in the efficient network, the dynamic model embedded in the structurally connected network and dynamic mean field(DMF) model predicting FC from SC. Extensive experiments on two different resolution datasets indicate that i) the dynamic model simulated on the shortest paths can predict FC among both structurally connected and unconnected node pairs; ii) though there are fewer links in the efficient propagation network, the predictive power of FC derived from the efficient propagation network is better than the dynamic model simulated on a structural brain network; iii) in comparison with the DMF model,the dynamic model embedded in the shortest paths is found to perform better to predict FC.
文摘Computed Tomography(CT)is a commonly used technology in Printed Circuit Boards(PCB)non-destructive testing,and element segmentation of CT images is a key subsequent step.With the development of deep learning,researchers began to exploit the“pre-training and fine-tuning”training process for multi-element segmentation,reducing the time spent on manual annotation.However,the existing element segmentation model only focuses on the overall accuracy at the pixel level,ignoring whether the element connectivity relationship can be correctly identified.To this end,this paper proposes a PCB CT image element segmentation model optimizing the semantic perception of connectivity relationship(OSPC-seg).The overall training process adopts a“pre-training and fine-tuning”training process.A loss function that optimizes the semantic perception of circuit connectivity relationship(OSPC Loss)is designed from the aspect of alleviating the class imbalance problem and improving the correct connectivity rate.Also,the correct connectivity rate index(CCR)is proposed to evaluate the model’s connectivity relationship recognition capabilities.Experiments show that mIoU and CCR of OSPC-seg on our datasets are 90.1%and 97.0%,improved by 1.5%and 1.6%respectively compared with the baseline model.From visualization results,it can be seen that the segmentation performance of connection positions is significantly improved,which also demonstrates the effectiveness of OSPC-seg.
