Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi‐level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyse literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.

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If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central

Mechanical properties such as hardness and modulus of sodium borosilicate (NBS) glasses in irradiation conditions were studied extensively in recent years. With irradiation of heavy ions, a trend that the hardness of NBS glasses decreased and then stabilized with increase of dose has been reported. Variations in network structures were suggested for the decrease of hardness after irradiation. However, details of these variations in a network of glass are not clear yet. In this paper, molecular dynamics was applied to simulate the network variations in a type of NBS glass and the changes in hardness after xenon irradiation. The simulation results indicated that hardness variation decreased with fluence in an exponential law, which was consistent with experimental results. The origin of hardness decrease after irradiation might be attributed to the break of B-O links that could be derived from the (1) decrease of average coordinate number of boron, (2) decrease of Si-O-B bonds, and (3) increase of non-bridging oxygen.