6results about How to "Improve segmentation performance" patented technology

The invention discloses an intelligent forest stand parameter extraction system based on laser radar point cloud data, which belongs to the technical field of laser radar point cloud data processing and forest resource investigation and comprises a multi-scale point cloud preprocessing module, a semantic guidance depth segmentation module, an intelligent parameter calculation module and a self-adaptive quality optimization module. High-precision and high-reliability intelligent extraction of forest stand parameters is realized through deep learning semantic segmentation of a Transform architecture, parameter calculation of confidence coefficient weighting, uncertainty quantification of Monte Carlo sampling and adaptive quality optimization of closed-loop feedback, a deep coupling relationship is formed among modules, the segmentation confidence coefficient directly affects the parameter weight, and the reliability of the forest stand parameters is improved. The uncertainty index triggers closed-loop adjustment, the adjustment parameter is fed back to the front module, a closed-loop optimization path of preprocessing, segmentation, parameter calculation, quality evaluation, adaptive adjustment and reprocessing is formed, and the precision and robustness of forest stand parameter extraction under a complex forest stand structure are remarkably improved.

This invention discloses a one-step generative segmentation method, system, and medium for 3D medical images based on the average flow model, belonging to the field of medical image processing technology. The invention acquires the 3D medical image to be segmented and anatomical condition information, samples Gaussian noise as an initial latent variable, and inputs it into a MeanFlow network after temporal embedding. Anatomical conditions are input into a VeloMod module to generate scale and offset tensors, and the MeanFlow network features are modulated pixel-by-pixel. The average velocity field is calculated using the average flow identity, and target distribution features are generated through one-step mapping. A 3D segmentation mask aligned with the original image space is output by a 3D decoder. This invention achieves single-step function evaluation and inference, significantly improving segmentation speed and anatomical fidelity. It possesses advantages such as small-sample generalization, multimodal robustness, missing modality compatibility, and strong interpretability, meeting the needs of real-time clinical navigation, intraoperative planning, and high-throughput screening. It has significant application value in the field of intelligent 3D medical image segmentation.

The invention relates to the field of computer vision and camouflage target segmentation, in particular to a camouflage target segmentation method of a reversible expansion network based on SAM guidance, which comprises the following steps of: firstly, constructing a foreground space priori graph, a background space priori graph and a high-quality SAM pseudo mask by utilizing a segmentation cutting model SAM; the prior redundancy is eliminated through low-dimensional orthogonal subspace projection, and the separability of the foreground and the background is enhanced; pixel-level and gradient-level feature fitting items and SAM subspace priori constraint items are fused to construct an overall objective function, the objective function is expanded into a multi-stage alternating iteration process of a foreground optimization submodule SFOS and a background optimization submodule SBOS, and a foreground feature map and a background feature map are refined step by step; and finally, generating a camouflage target segmentation mask according to the iteratively optimized foreground feature map. Through large model prior guidance, two-stage feature modeling and multi-stage expansion optimization, the integrity and accuracy of camouflage target segmentation are significantly improved.

The application discloses a SAM2 small sample segmentation method based on semantic-visual double memory fusion, constructs semantic query memory, visual query memory and query related support visual memory, fuses the semantic query memory and the visual query memory through a memory refinement module guided by the query related support visual memory, and combines SAM2 dense matching and decoding module end-to-end training. The method solves the single memory and foreground-background confusion problems of the existing method, enhances target semantic consistency and fine-grained modeling capability, significantly improves segmentation precision and generalization capability in a complex scene, and is suitable for medical image analysis, automatic driving and the like.

The application discloses a retinal cross-modal segmentation method and device based on domain-invariant contrast learning, relates to the technical field of medical image segmentation, and comprises the following steps: acquiring a retinal image; establishing a retinal cross-modal segmentation model; inputting the retinal image into the retinal cross-modal segmentation model to obtain a segmentation result of the retinal image, wherein the retinal cross-modal segmentation model is based on a U-net model as a basic framework, is trained by using image data of different modes, and is obtained through domain-invariant contrast learning and uncertainty perception self-integrated teacher framework. The application can effectively adapt to new modes and realize accurate image segmentation under limited labeled data.

The invention discloses an ultrasonic image defect segmentation method based on a memory bank-patch denoising diffusion probability model, and belongs to the field of ultrasonic nondestructive testing. The method is an ultrasonic image unsupervised automatic segmentation method which is based on a memory bank and does not need labels, a defect-free image is adopted to train an unsupervised anomaly detection model, and the trained model is used to carry out automatic defect segmentation on a defect-containing image. According to the method, the anomaly detection capability is improved through the memory bank module, automatic segmentation of ultrasonic image defects can be realized on the premise that data does not need to be labeled, and excellent segmentation performance is obtained. According to the method, the dependence on a large amount of manual annotation data can be effectively relieved, and a more accurate solution is provided for automatic segmentation of ultrasonic image defects.