STF-Net: sparsification transformer coding guided network for subcortical brain structure segmentation.

Subcortical brain structure segmentation plays an important role in the diagnosis of neuroimaging and has become the basis of computer-aided diagnosis. Due to the blurred boundaries and complex shapes of subcortical brain structures, labeling these structures by hand becomes a time-consuming and subjective task, greatly limiting their potential for clinical applications. Thus, this paper proposes the sparsification transformer (STF) module for accurate brain structure segmentation. The self-attention mechanism is used to establish global dependencies to efficiently extract the global information of the feature map with low computational complexity. Also, the shallow network is used to compensate for low-level detail information through the localization of convolutional operations to promote the representation capability of the network. In addition, a hybrid residual dilated convolution (HRDC) module is introduced at the bottom layer of the network to extend the receptive field and extract multi-scale contextual information. Meanwhile, the octave convolution edge feature extraction (OCT) module is applied at the skip connections of the network to pay more attention to the edge features of brain structures. The proposed network is trained with a hybrid loss function. The experimental evaluation on two public datasets: IBSR and MALC, shows outstanding performance in terms of objective and subjective quality.

EG-Unet: Edge-Guided cascaded networks for automated frontal brain segmentation in MR images.

Accurate segmentation of frontal lobe areas on magnetic resonance imaging (MRI) can assist in diagnosing and managing idiopathic normal-pressure hydrocephalus. However, frontal lobe segmentation is challenging due to the complexity of the degree and shape of damage and the ambiguity of the boundaries of frontal lobe sites. Therefore, to extract the rich edge information and feature representation of the frontal lobe, this paper designs an edge guidance (EG) module to enhance the representation of edge features. Accordingly, an edge-guided cascade network framework (EG-Net) is proposed to segment frontal lobe parts automatically. Two-dimensional MRI slice images are fed into the edge generation and segmentation networks. First, the edge generation network extracts the edge information from the input image. Then, the edge information is sent to the EG module to generate an edge attention map for feature representation enhancement. Meanwhile, multi-scale attentional convolution (MSA) is utilized in the feature coding stage of the segmentation network to obtain feature responses from different perceptual fields in the coding stage and enrich the spatial context information. Besides, the feature fusion module is employed to selectively aggregate the multi-scale features in the coding stage with the edge features output by the EG module. Finally, the two components are fused, and a decoder recovers the spatial information to generate the final prediction results. An extensive quantitative comparison is performed on a publicly available brain MRI dataset (MICCAI 2012) to evaluate the effectiveness of the proposed algorithm. The experimental results indicate that the proposed method achieves an average DICE score of 95.77% compared to some advanced methods, which is 4.96% better than the classical U-Net. The results demonstrate the potential of the proposed EG-Net in improving the accuracy of frontal edge pixel classification through edge guidance.

Relationship of five anthropometric indices and blood pressure in an adult Chinese population.

The study examined the relationship of blood pressure (BP) with anthropometric indices, including body mass index (BMI), waist-to-hip ratio (WHR), conicity index (CI), waist circumference (WC), and hip circumference (HC). The sample consisted of 731 females and 911 males aged 20-60 years randomly recruited from Shanxi Province of PR China. There was an increasing trend of systolic blood pressure (SBP), diastolic blood pressure (DBP), and the five anthropometric indices in successively older age groups. Except for female HC, all the other anthropometric indices were significantly correlated with BPS. Among them, WC and HC exhibited the highest correlations in female (0.38-0.42) and in male (0.36-0.37), respectively. The result indicates that HC is not protective for metabolic risk factors in males. Principal component analysis (PCA) was performed on all the five anthropometric indices and four major principal components (PCs) were obtained to explain greater variances (R(2) = 0.173-0.212) in BPS than did anyone of five anthropometric indices (R(2) = 0.0001-0.176). The same analyses were conducted on any two, three, and four indices of the five anthropometric ones, in order to get the smaller and optimal sets of indices to estimate BPs. Our results suggest that BMI, WC, and HC are the smallest set of anthropometric indices to optimally estimate BP for the males, and WC and BMI for the females.