Objective To observe the value of 2D SECara-Net and 3D U2-Net models constructed based on 2D maximal intensity projection(MIP)and 3D time-of-flight MR angiography(3D TOF-MRA)images,respectively,also of their combination for MRA detecting unruptured saccular intracranial aneurysms(USIA).Methods Totally 973 patients with single USIA and 300 subjects who underwent healthy physical examination were retrospectively collected and divided into training set(n=923,containing 723 cases of USIA and 200 healthy subjects)and test set(n=350,containing 250 cases of USIA and 100 healthy subjects)at the ratio of 7:3.Pre-processed 3D TOF-MRA and the obtained 2D-MIP images in training set were imported into 3D U2-Net and 2D SECara-Net models for training and adjusting parameters,respectively.The efficiency of 2 models and their combination for detecting USIA were evaluated.Results The sensitivity,specificity and accuracy of 2D SECara-Net model for detecting USIA in test set was 78.80％(197/250),95.00％(95/100)and 83.43％(292/350),of 3D U2-Net model was 82.80％(207/250),86.00％(86/100)and 83.71％(293/350),respectively.The specificity of 2D SECara-Net model was higher than that of 3D U2-Net model(P=0.030),while no significant difference of sensitivity nor accuracy was found between 2 models(both P＞0.05).The specificity of the combination of the 2 models was 99.00％(99/100),higher than that of 3D U2-Net model(P＜0.05),and the sensitivity and accuracy of the combination was 91.20％(228/250)and 93.43％(327/350),respectivelty,both higher than those of 2 single models(all P＜0.05).Conclusion 2D SECara-Net and 3D U2-Net models had similar,sensitivity and accuracy for MRA detecting USIA.Combination of them could improve the detecting efficacy.

Objective To observe the value of 2D SECara-Net and 3D U2-Net models constructed based on 2D maximal intensity projection(MIP)and 3D time-of-flight MR angiography(3D TOF-MRA)images,respectively,also of their combination for MRA detecting unruptured saccular intracranial aneurysms(USIA).Methods Totally 973 patients with single USIA and 300 subjects who underwent healthy physical examination were retrospectively collected and divided into training set(n=923,containing 723 cases of USIA and 200 healthy subjects)and test set(n=350,containing 250 cases of USIA and 100 healthy subjects)at the ratio of 7:3.Pre-processed 3D TOF-MRA and the obtained 2D-MIP images in training set were imported into 3D U2-Net and 2D SECara-Net models for training and adjusting parameters,respectively.The efficiency of 2 models and their combination for detecting USIA were evaluated.Results The sensitivity,specificity and accuracy of 2D SECara-Net model for detecting USIA in test set was 78.80％(197/250),95.00％(95/100)and 83.43％(292/350),of 3D U2-Net model was 82.80％(207/250),86.00％(86/100)and 83.71％(293/350),respectively.The specificity of 2D SECara-Net model was higher than that of 3D U2-Net model(P=0.030),while no significant difference of sensitivity nor accuracy was found between 2 models(both P＞0.05).The specificity of the combination of the 2 models was 99.00％(99/100),higher than that of 3D U2-Net model(P＜0.05),and the sensitivity and accuracy of the combination was 91.20％(228/250)and 93.43％(327/350),respectivelty,both higher than those of 2 single models(all P＜0.05).Conclusion 2D SECara-Net and 3D U2-Net models had similar,sensitivity and accuracy for MRA detecting USIA.Combination of them could improve the detecting efficacy.

Objective To investigate the temporal properties of dynamic functional connectivity of brain networks and the variability of network topology in patients with end-stage renal disease(ESRD).Methods Data of 30 ESRD patients(ESRD group)and 33 healthy subjects(control group)were retrospectively analyzed.Based on cranial resting-state functional MRI(rs-fMRI),dynamic functional connectivity(dFC)and graph theory analysis were employed,and the abnormalities in network topology and dFC in ESRD patients were assessed through comparison of groups.Pearson correlation analysis was used to observe the correlation between abnormal dFC indicators and clinical variables.Results Compared with control group,temporal scores and the mean residence time in ESRD group were significantly higher under state Ⅱ but significantly lower under state Ⅲ(both P＜0.05).The abnormal functional connectivity in ESRD patients under states Ⅱ and Ⅲ distributed mainly within and between default mode network,sensorimotor network,subcortical nuclei,execution and attention network,visual network and cerebellum networks.Network density and bilateral superior temporal gyrus nodal degrees in ESRD group were all significantly lower than those in control group(all P＜0.05).No significant correlation was found between the abnormal parameters of functional connectivity and graph theory attributes in ESRD group and clinical indicators under states Ⅱ nor Ⅲ(all P＞0.05).Conclusion ESRD patients had abnormal temporal attributes and network topology of brain dynamic networks related to cognitive impairments.

In order to overcome the difficulty in lung parenchymal segmentation due to the factors such as lung disease and bronchial interference, a segmentation algorithm for three-dimensional lung parenchymal is presented based on the integration of surfacelet transform and pulse coupled neural network (PCNN). First, the three-dimensional computed tomography of lungs is decomposed into surfacelet transform domain to obtain multi-scale and multi-directional sub-band information. The edge features are then enhanced by filtering sub-band coefficients using local modified Laplacian operator. Second, surfacelet inverse transform is implemented and the reconstructed image is fed back to the input of PCNN. Finally, iteration process of the PCNN is carried out to obtain final segmentation result. The proposed algorithm is validated on the samples of public dataset. The experimental results demonstrate that the proposed algorithm has superior performance over that of the three-dimensional surfacelet transform edge detection algorithm, the three-dimensional region growing algorithm, and the three-dimensional U-NET algorithm. It can effectively suppress the interference coming from lung lesions and bronchial, and obtain a complete structure of lung parenchyma.
Algorithms ; Neural Networks, Computer ; Tomography, X-Ray Computed

Mild encephalitis/encephalopathy with reversible splenial lesion has special clinical-imaging features. According to the extent of lesion involvement, it can be divided into type Ⅰ and type Ⅱ. Clinically, type Ⅰ is more common, and type Ⅱ is rare. A rare case of recurrent type Ⅱ mild encephalitis/encephalopathy with reversible splenial lesion is reported. The patient presented with typical type Ⅱ mild encephalitis/encephalopathy with reversible splenial lesion for the first time, involving the corpus callosum and the deep white matter, and the lesions disappeared after a short-term reexamination. Two years later, the lesions recurred, and the scope of the lesions was similar to that of the first time, and the lesions disappeared after a short-term reexamination. The clinical and imaging findings are analyzed in combination with relevant literatures review in order to deepen the understanding of the disease and improve the level of diagnosis and treatment.

Objective To analyze the computed tomography (CT)characteristics of splenic lymphangioma and the association be-tween CT findings and pathological results.Methods The CT characteristics and pathological findings of 9 patients with splenic lym-phangioma were retrospectively analyzed.Results There were 8 cavernous lymphangioma and 1 cystic lymphangioma.Of the 9 ca-ses,it revealed that the percentage of blood vessel elements ranging from 5% to 50% via microscope.CT found 2 cases were with single lymphangioma,3 with multiple lesions,exhibiting as round or round-like mass;4 cases were found with diffuse lymphangio-ma and different size of cystic masses distributed across spleen.Five of the 9 cases who were with single or multiple lymphangioma showed circular and thin line-like cyst wall,while the remaining 4 cases showed latticed cyst wall in CT characteristics.The content in the cysts were with uneven density in all the 9 cases with CT value ranging from 10 to 40 HU,3 of which combined with sand-like calcification.Enhancement scanning found two characteristics:(a)cyst wall and separation were mildly enhanced,especially in the delayed phase;(b)the content in the cyst presents anomalous small patchy and mild enhancement.The enhancement of the content in the cyst did not change as the increasing of blood vessel composition.Conclusion CT examination will help the diagnosis of splenic lymphangioma and is of significance in informing clinical treatment.