Automatic and reliable segmentation of the pancreas is an important but difficult task for various clinical applications, such as pancreatic cancer radiotherapy and computer-aided diagnosis (CAD). The main challenges for accurate CT pancreas segmentation lie in two aspects: (1) large shape variation across different patients, and (2) low contrast and blurring around the pancreas boundary. In this paper, we propose a two-stage, ensemble-based fully convolutional neural network (FCN) to solve the challenging pancreas segmentation problem in CT images. First, candidate region generation is performed by classifying patches generated by superpixels. Second, five FCNs based on the U-Net architecture are trained with different objective functions. For each network, 2.5D slices are used as the input to provide 3D image information complementarily without the need for computationally expensive 3D convolutions. Then, an ensemble model is utilized to combine the five output segmentation maps and achieve the final segmentation. The proposed method is extensively evaluated on a publicly available dataset of 82 manually segmented CT volumes via 4-fold cross-validation. Experimental results show its superior performance compared with several state-ofthe-art methods with a Dice coefficient of 84.10±4.91% and Jaccard coefficient of 72.86±6.89%.
Objective The network connectivity basis of cognitive declines in end-stage renal disease (ESRD) remains unclear. A triple-network model of the salience (SN), executive control, and default mode networks has been suggested to be critical for efficient cognition. Here, we aimed to test the hypothesis that SN may play a role in cognitive impairment in patients with ESRD. Materials and Methods We investigated functional connectivity (FC) alterations within the SN between 43 ESRD patients (19 females/24 males, 46 ± 10 years) and 43 healthy controls (HC) (19 females/24 males, 47 ± 10 years), and performed linear support vector machine (LSVM) analysis on significant FC pairs within the SN to discriminate the two groups, and tested the accuracy of the classifier. Association and mediation analyses were conducted among the significant FC pairs within the SN nodes, clinical indicators, and neuropsychological tests scores. Results We identified significant between-group FC pairs within the SN and fairly good classification efficiency with significant accuracy (72.09%, p < 0.001). We found that FC between the right supramarginal gyrus and right anterior insula (AISL) was positively correlated with MoCA ( r = 0.4010, p = 0.008); FC between the dorsal anterior cingulate cortex (dACC) and left AISL was positively correlated with the level of hemoglobin ( r = 0.4979, p < 0.001). Mediation analysis found that the indirect effect of hemoglobin on forward digit span test scores via the FC between the dACC and right AISL ( p < 0.05). Conclusion Disrupted SN connectivity may help explain cognitive declines in ESRD patients and act as a potential early biomarker. Moreover, the SN connectivity may interact with anemia to promote cognitive impairment.
Objectives. Our study aims to determine the patterns of renal oxygenation changes and microstructural changes by BOLD and DTI with deteriorating kidney function in patients with diabetic kidney disease (DKD). Methods. Seventy-two patients with type 2 diabetes mellitus (DM) and twenty healthy controls (HCs) underwent laboratory examinations, and renal BOLD and DTI images were obtained on a 3T-MRI machine. R 2 ∗ , fractional anisotropy (FA), and average diffusion coefficient (ADC) values were evaluated. DM patients were divided into three subgroups (Group-DI/DII/DIII, based on urinary albumin-creatinine ratio (UACR)) and a nondiabetic kidney disease group (Group-NDKD). D-value and MCR of R 2 ∗ and FA were proposed to evaluate the differentiation between medulla and cortex of the individual kidney among HCs and three subgroups for reducing individual differences. Comparisons were made between NDKD and kidney function-matched DKD patients. Correlations between MRI parameters and renal clinical indices were analyzed. Results. Compared with Group-HC/DI, medullary R 2 ∗ and FA values were significantly different in Group-DII/III. The D-value of R 2 ∗ and FA in Group-III were significantly smaller than that in Group-HC. However, only MCR of R 2 ∗ in Group-III was significantly smaller than that in HCs. Medullary R 2 ∗ and FA were negatively associated with serum creatinine (SCr) and cystatin C (Cys C) and positively associated with eGFR. Conclusions. With renal function declining, BOLD and DTI could capture alterations including the first rising and then falling medullary R 2 ∗ , continuously declining medullary FA, and apparent cortex-medullary differentiation in DKD patients. The MRI parameters showed renal changes accompanied by varying degrees of albuminuria, sharing common involvement in DKD and NDKD patients, but it was hard to distinguish between them. BOLD seemed more sensitive than DTI in identifying renal cortex-medullary differentiation.
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