Automatic segmentation of liver tumors is crucial to assist radiologists in clinical diagnosis. While various deep learning-based algorithms have been proposed, such as U-Net and its variants, the inability to explicitly model long-range dependencies in CNN limits the extraction of complex tumor features. Some recent researchers have applied Transformer-based 3D networks to analyze medical images. However, the previous methods focus on modeling the local information (eg. edge) or global information (eg. morphology) with fixed network weights. To learn and extract complex tumor features of varied tumor size, location, and morphology for more accurate segmentation, we propose a Dynamic Hierarchical Transformer Network, named DHT-Net. The DHT-Net mainly contains a Dynamic Hierarchical Transformer (DHTrans) structure and an Edge Aggregation Block (EAB). The DHTrans first automatically senses the tumor location region by Dynamic Adaptive Convolution, which employs hierarchical operations with the different receptive field sizes to learn the features of various tumors, thus enhancing the semantic representation ability of tumor features. Then, to adequately capture the irregular morphological features in the target tumor region, DHTrans aggregates global tumor shape and local texture information in a complementary manner. In addition, we introduce the EAB to extract detailed edge features in the shallow fine-grained details of the network, which provides sharp boundaries of liver tissue and tumor regions. We evaluate our method on two challenging public datasets, LiTS and 3DIRCADb. The proposed method has shown superior liver and tumor segmentation performance compared to several state-of-the-art 2D, 3D, and 2.5D hybrid models. The code is available at https://github.com/Lry777/DHT-Net

Infrared object tracking is an important subfield of computer vision that has a wide range of applications in harsh tracking environments. However, the absence of color information and blurred video frames make infrared object tracking more challenging than RGB tracking. To make full use of temporal information in infrared (IR) video and improve tracking accuracy, we propose a temporal features fusion mechanism that can fuse target features extracted by Siamese networks at different moments while tracking, thus integrating the temporal information to improve the accuracy. Weights for each target feature are calculated separately based on channel and spatial aspects and then aggregated. This enables element-wise feature fusion with fewer parameters and reduced computational cost. In addition, we propose an online feature update method, which collects target information online through a target-specific network and evaluates the results of each tracking to obtain high-quality tracking samples for temporal feature fusion. Our method exhibits state-of-the-art tracking performance compared to other existing tracking methods. In addition, we analyze the influence of several main factors on temporal fusion networks through an ablation study.

Residual stress regulation technology is a procedure to reduce residual stress by means of high-energy ultrasonic after stress testing. Based on acoustoelasticity theory, the relationship between ultrasonic propagation and stress in aluminum alloy is researched. With the principle of exciting ultrasonic critically refracted longitudinal wave obtained, the ultrasonic stress testing system is established. For in situ regulation of residual stress, the interactive relationship between high energy ultrasonic and residual stress field is studied. We reduced the residual stress and improved stress state of components through effective control of high-energy beam and incentive mode. Thus in situ relief and control of local residual stress distribution in mechanical components in service are realized. The technology can improve the whole strength, anti-fatigue and corrosion resistance of mechanical components and enhance their service life, safety and reliability, and now is widely used in the stress testing and relief of aluminum alloy welding components.