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Glioma is a type of brain tumor with a high mortality rate. Magnetic resonance imaging (MRI) is commonly used for examination, and the accurate segmentation of tumor regions from MR images is essential to computer‐aided diagnosis. However, due to the intrinsic heterogeneity of brain glioma, precise segmentation is very challenging, especially for tumor subregions. This article proposed a two‐stage cascaded method for brain tumor segmentation that considers the hierarchical structure of the target tumor subregions. The first stage aims to identify the whole tumor (WT) from the background area; and the second stage aims to achieve fine‐grained segmentation of the subregions, including enhanced tumor (ET) region and tumor core (TC) region. Both stages apply a deep neural network structure combining modified 3D U‐Net with a residual connection scheme to tumor region and subregion segmentation. Moreover, in the training phase, the 3D masks generation of subregions with potential incomplete connectivity are guided by the completely connected regions. Experiments were performed to evaluate the performance of the methods on both area and boundary accuracy. The average dice score of the WT, TC, and ET regions on BraTS 2020 dataset is 0.9168, 0.0.8992, 0.8489, and the Hausdorff distance is 6.021, 9.203, 12.171, respectively. The proposed method outperforms current works, especially in segmenting fine‐grained tumor subregions. [ABSTRACT FROM AUTHOR]

Adverse delivery outcomes is a major re-productive health problem that affects the physical and mental health of pregnant women. Obviously, obstetric clinical data has periodically time series characteristics. This paper proposed a three stage adverse delivery outcomes prediction model via the fusion of multiple time series clinical data. The first stage is data aggregation, in which the data set is collected from the obstetric clinical data and divided based on time series features. In the second stage, a multi-channel gated cycle unit is used to solve the calculation error caused by irregular sampling of time series data. The hidden layer feature vector is connected with the fully connected layer, reshaped into a new one-dimensional feature, and fused with the non-time series data into a new data set. The third stage is the prediction stage of adverse delivery outcomes. By connecting the multi-channel gated cycle unit with the extreme gradient lift, the data transmitted in the corresponding channel is used in the feature extraction stage, in which the weighted entropy-based feature extraction is adopted. With the help of the extracted features, a hybrid artificial neural network architecture (MGRU-XGB) was developed to predict adverse delivery outcomes. The experimental results showed that the hybrid model had the best prediction performance for adverse delivery outcomes compared with other single models in terms of sensitivity, specificity, AUC and other evaluation indexes. [ABSTRACT FROM AUTHOR]

Fetal abdominal subcutaneous soft tissue thickness (FASSTT) is a key indicator in evaluating fetal growth, development, and nutritional status. Currently, manual measurement in FASSTT faces the problems of difficulty in positioning, time consumption, and inaccurate measurement. Therefore, this article proposes an automatic measurement scheme for FASSTT. Firstly, a U‐shaped attention network VGG‐SeUnet is proposed to automatically segment the subcutaneous soft tissue area of the fetal abdomen. Secondly, based on the segmentation results, a morphological method is proposed to obtain FASSTT. Specifically, the segmentation network uses VGG16 as the encoder and connects the decoder through jump connections for multi‐scale fusion. We introduce channel attention to jump connections, which enables the model to select key channels for feature fusion. At the same time, the model uses the proposed DF_Loss function for training to solve the problem of sample imbalance. Based on the segmentation results, a morphological distance transformation algorithm is proposed to obtain FASSTT by drawing the maximum inscribed circle and calculating its diameter. The method is evaluated on a fetal abdominal circumference ultrasound dataset containing 135 samples. The DSC, mIOU, mPA, Precision and Recall of the segmentation experiments reache 88.9%, 81.02%, 90.44%, 86.38%, and 90.44% respectively. The difference between FASSTT's result from that provided by radiologist is 0.0615 cm in Average Error (AVE) and 0.081 cm in Root Mean Square Error (RMSE). The overall performance of this algorithm is superior to existing methods with excellent performance, and the measurement error is less than 1 millimeter. This result proves that the proposed scheme in this paper can achieve automated measurement of FASSTT and assist doctors in subsequent evaluation of fetal development and nutritional status. [ABSTRACT FROM AUTHOR]