ISA-Net: Improved spatial attention network for PET-CT tumor segmentation

Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. Conclusions: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment

Genesis and Accumulation Period of CO₂ Gas Reservoir in Hailar Basin

Gas reservoirs with high CO2 have been found in several wells in the Hailar Basin. In this paper, a composition analysis, stable carbon isotope analysis, and a rare gas helium isotope 3He/4He and argon isotope 40Ar/36Ar analysis were carried out. These comprehensive analyses show that the CO2 in the Hailar Basin is inorganic-origin gas, which generally has the characteristics of crust–mantle-mixed CO2, and the fraction of helium of mantle source can reach 15.12~18.76%. There are various types of CO2 gas reservoirs. CO2 gas mainly comes from deep crust. The distribution of gas reservoirs is mainly controlled by deep faults and volcanic rocks, as well as by reservoir properties and preservation conditions. Magmatic rocks provide gas source conditions for the formation of inorganic CO2 reservoirs. Deep–large faults provide the main migration channels for CO2 gas. The sandy conglomerate and bedrock weathering crust of the Nantun Formation and the Tongbomiao Formation provide favorable reservoir spaces for the formation of CO2 gas reservoirs. The combination of volcanic rock mass and deep–large faults creates a favorable area for CO2 gas accumulation. The age of magmatic intrusion and the homogenization temperature of oil–gas inclusions in Dawsonite-bearing sandstone indicate that 120 Ma in the Early Cretaceous was the initial gas generation period of the CO2 reservoir and that oil and gas were injected into the reservoir in large quantities in 122~88 Ma. This period is the peak period of magmatic activity in Northeast China, as well as when the crust of Northeast China greatly changed. A large-scale CO2 injection period occurred in 100~80 Ma, slightly later than the large-scale injection period of the oil and gas. Since the Cenozoic, the structure has been reversed, and the gas reservoir has been adjusted

Effects of Diisocyanate Structure and Disulfide Chain Extender on Hard Segmental Packing and Self-Healing Property of Polyurea Elastomers

Four linear polyurea elastomers synthesized from two different diisocyanates, two different chain extenders and a common aliphatic amine-terminated polyether were used as models to investigate the effects of both diisocyanate structure and aromatic disulfide chain extender on hard segmental packing and self-healing ability. Both direct investigation on hard segments and indirect investigation on chain mobility and soft segmental dynamics were carried out to compare the levels of hard segmental packing, leading to agreed conclusions that correlated well with the self-healing abilities of the polyureas. Both diisocyanate structure and disulfide bonds had significant effects on hard segmental packing and self-healing property. Diisocyanate structure had more pronounced effect than disulfide bonds. Bulky alicyclic isophorone diisocyanate (IPDI) resulted in looser hard segmental packing than linear aliphatic hexamethylene diisocyanate (HDI), whereas a disulfide chain extender also promoted self-healing ability through loosening of hard segmental packing compared to its C-C counterpart. The polyurea synthesized from IPDI and the disulfide chain extender exhibited the best self-healing ability among the four polyureas because it had the highest chain mobility ascribed to the loosest hard segmental packing. Therefore, a combination of bulky alicyclic diisocyanate and disulfide chain extender is recommended for the design of self-healing polyurea elastomers