Three-dimensional blood vessel segmentation is an important and challenging task that faces two main difficulties: (1) blood vessel structures are small, making them hard to capture by the network, and vessel edges are difficult to segment accurately; (2) false positives are prone to occur due to the presence of artifacts and noise. This paper proposes a novel blood vessel segmentation method called MambaVesselNet. This method is based on a state-space model and employs a selective state-space time series modeling strategy to achieve a larger receptive field. To better capture fine vascular structures and accurately segment edges, this paper introduces an edge enhancement module and a feature selection module. In terms of data preprocessing, nnUNet's preprocessing strategy is adopted to ensure spatial consistency of the input data. Evaluation on three standard vascular segmentation benchmarks shows that MambaVesselNet achieves state-of-the-art performance. Specifically, on cardiovascular and liver vessel datasets, the Dice coefficient is improved by 1.38% and 2.69%, respectively. The contributions of this paper include the proposal of a new module for enhancing blood vessel edge features, the development of a feature selection module with long sequence modeling capability, and the adoption of nnUNet's data preprocessing strategy, setting a new benchmark for blood vessel segmentation technology.

Total organic carbon (TOC) content is an important parameter for evaluating the abundance of organic matter in, and the hydrocarbon production capacity, of shale. Currently, no prediction method is applicable to all geological conditions, so exploring an efficient and accurate prediction method suitable for the study area is of great significance. In this study, for the shale of the Qingshankou Formation of the Gulong Sag in the Songliao Basin, TOC content prediction models using various machine learning algorithms are established and compared based on measured data, principal component analysis, and the particle swarm optimization algorithm. The results showed that GR, AC, DEN, CNL, LLS, and LLD are the most sensitive parameters using the Pearson correlation coefficient. The four principal components were also identified as input features through PCA processing. The XGBoost prediction model, established after selecting the parameters through PSO intelligence, had the highest accuracy with an $R^2$ and RMSE of 0.90 and 0.1545, respectively, which are superior to the values of the other models. This model is suitable for the prediction of TOC content and provides effective technical support for shale oil exploration and development in the study area.

With the rapid development of digital core technology, the acquisition of high-resolution rock thin section images has become crucial. Due to the limitation of optical principles, thin section imaging involves a contradiction between resolution and field of view. In order to solve this problem, this paper proposes a lightweight, fully aggregated network with multi-branch structure for super resolution of rock thin section images. The experimental results on the rock thin section dataset demonstrate that the improved method, called OmniSR-M, achieves significant enhancement compared to the original OmniSR method and also surpasses other state-of-the-art methods. OmniSR-M effectively recovers image details while maintaining its lightweight nature. Specifically, OmniSR-M reduces the number of parameters by 26.56% and the computation by 27.66% compared to OmniSR. Moreover, this paper quantitatively analyzes both the facies porosity rate and grain size features in the application scenario. The results show that the images generated by OmniSR-M successfully recover key information about the rock thin section.

Spinal medical image segmentation is critical for diagnosing and treating spinal disorders. However, ambiguity in anatomical boundaries and interfering factors in medical images often cause segmentation errors. Current deep learning models cannot fully capture the intrinsic data properties, leading to unstable feature spaces. To tackle the above problems, we propose Verdiff-Net, a novel diffusion-based segmentation framework designed to improve segmentation accuracy and stability by learning the underlying data distribution. Verdiff-Net integrates a multi-scale fusion module (MSFM) for fine feature extraction and a noise semantic adapter (NSA) to refine segmentation masks. Validated across four multi-modality spinal datasets, Verdiff-Net achieves a high Dice coefficient of 93%, demonstrating its potential for clinical applications in precision spinal surgery.