17 results about "Semi automatic segmentation" patented technology

The invention relates to the technical field of crop monitoring, in particular to a crop phenotypic parameter automatic calculation and extraction method based on a multi-source remote sensing image. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing automatic or semi-automatic segmentation on the images determined in the same coordinate system to obtain a multi-source crop remote sensing image cell segmentation map; extracting plant phenotypic parameters, physical parameters and chemical parameters from the multisource crop remote sensing image cell segmentation map by using an algorithm; the plant phenotype parameters comprise a vegetation index, a plant height, a surface area, a volume, a canopy coverage degree and a vegetation projection area; the physical parameters comprise a canopy average temperature value, a canopy temperature standard deviation and a canopy temperature variation coefficient; the chemical parameters comprise chemical elements such as nitrogen, phosphorus, potassium, calcium and magnesium in soil and vegetation. The method has the advantages that large-scale data processing and high-precision area prediction are realized, and the crop growth monitoring capability is enhanced.

The invention relates to the technical field of crop monitoring, in particular to an automatic cell segmentation method for a crop remote sensing image based on geographic prior information. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing automatic segmentation or semi-automatic segmentation on the images determined in the same coordinate system to obtain a cell segmentation map of the multi-source crop remote sensing image; performing semi-automatic segmentation on an unplanted bare soil region and a region of crops with unknown growth vigor; aiming at a crop area with known growth vigor, carrying out batch full-automatic segmentation processing on the multi-source sensor image by adopting an improved Ground-SAM segmentation large model, and outputting a segmentation result; and carrying out optimization processing on the segmentation result, and finally outputting an optimized cell segmentation map of the multi-source crop remote sensing image. The method has the advantages of accurate registration, efficient segmentation, multi-sensor compatibility and automatic processing, and remote sensing monitoring is improved.

The application discloses a kind of nnUNet zebra fish juvenile whole brain vascular system segmentation methods based on autonomous data set training, it is related to high-resolution imaging technology, image processing and medical image segmentation field, the method makes full use of zebra fish live transparency and fluorescent label advantage, obtains high-resolution whole brain three-dimensional vascular image data, and constructs high-quality segmentation truth value database by semi-automatic segmentation and artificial correction, training is carried out using nnU-Net deep learning model, realize the three-dimensional automatic segmentation of zebra fish brain vascular system signal.The application method significantly improves the degree of automation and precision of image segmentation, effectively solves the problems of low efficiency, high artificial dependence and poor repeatability of traditional brain vascular segmentation.The method is suitable for large-scale high-throughput data processing, can provide efficient, standardized image processing scheme for zebra fish brain vascular development mechanism and brain vascular disease model research, and has wide application prospect.

The present invention relates to the field of crop monitoring technology, and more particularly to a method for automatically calculating and extracting crop phenotypic parameters based on multi-source remote sensing images. The method includes aligning all images to the same coordinate system through georeferencing, point cloud file conversion, and ground-specific point matching to achieve image registration; automatically or semi-automatically segmenting the images aligned to the same coordinate system to obtain a multi-source crop remote sensing image plot segmentation map; and using an algorithm to extract plant phenotypic, physical, and chemical parameters from the multi-source crop remote sensing image plot segmentation map. Plant phenotypic parameters include vegetation index, plant height, surface area, volume, canopy cover, and vegetation projected area; physical parameters include canopy mean temperature, canopy temperature standard deviation, and canopy temperature coefficient of variation; and chemical parameters include soil and vegetation chemical elements such as nitrogen, phosphorus, potassium, calcium, and magnesium. The method has the advantage of enabling large-scale data processing and high-precision regional prediction, enhancing crop growth monitoring capabilities.

One aspect of the invention concerns a method for characterizing the internal three-dimensional organization of a biological tissue sample comprising a plurality of types of biological elements (201, 202), said method having the following steps:For at least one type of biological elements (201, 202, 203, 204) of interest among the plurality of types of biological elements (201, 202, 203, 204), automatic or semi-automatic segmentation in each image (IZ) from a stack of images (I3D), of at least one region containing at least one biological element (201, 202, 203, 204) having as type, the type of biological elements (201, 202,203, 204) of interest, the stack of images (I3D) having been acquired by Z-series imaging by automated ultramicrotomy under scanning electron microscopy and including a plurality of images (IZ) each acquired in a plane perpendicular to a depth axis (Z) and each associated with a position on the depth axis (Z), the plurality of images (IZ) being ordered by increasing position in the stack of images (I3D, 102);Characterization of a set of biological elements (201, 202, 203, 204) having as type the type of biological elements (201, 202, 203, 204) of interest, by calculation, for each biological element (201, 202, 203, 204) from the set of biological elements (201, 202, 203, 204), of at least one indicator (301, 302) relating to the structure, the morphology, the size, the polarity, the texture, the constitution, the orientation, a surface area, the alignment, the convergence, the density, the convexity or the concavity of the biological element (201, 202, 203,204), from each corresponding segmented region (104);Comparison between the indicators (301, 302) calculated for the set of biological elements (201, 202, 203, 204).

This invention provides a method and system for turbine blade CT image segmentation and point detection, relating to the field of image processing. The method includes: S1, acquiring industrial CT images of turbine blades and constructing a dataset; S2, performing semi-automatic segmentation and annotation, as well as key point annotation; S3, performing image flipping, rotation, and contrast adjustment operations for data enhancement; S4, constructing an image segmentation and point detection model; S5, designing a loss function to train a multi-task learning model; and S6, model training, testing, and output. The system includes an image acquisition module, an image annotation module, a segmentation and point detection model construction module, and a segmentation and point detection module. This invention uses multi-task learning to simultaneously segment and detect points in industrial CT images of turbine blades, achieving automated and high-precision detection of turbine blade wall thickness parameters, and providing a reliable digital solution for quality control and process optimization of key components of aero-engines.

The invention provides a turbine blade CT image segmentation and point location detection method and system, and relates to the field of graphic processing, and the method comprises the steps: S1, collecting a turbine blade industrial CT image, and constructing a data set; s2, performing semi-automatic segmentation labeling and key point labeling; s3, turning over and rotating the image, and adjusting the contrast to enhance the data; s4, constructing an image segmentation and point detection model; s5, designing a loss function to train a multi-task learning model; and S6, training, testing and outputting the model. The system comprises an image acquisition module, an image labeling module, a segmentation and point location detection model construction module and a segmentation and point location detection module. According to the method, segmentation and point location detection are simultaneously carried out on the industrial CT image of the turbine blade by using multi-task learning, automatic high-precision detection of the wall thickness parameter of the turbine blade is realized, and a reliable digital solution is provided for quality control and process optimization of key parts of an aero-engine.

The present invention relates to the field of crop monitoring technology, and in particular to a method for automatic segmentation of crop remote sensing images based on geographic prior information. The method comprises: determining all images in the same coordinate system through geographic registration, point cloud file conversion, and ground specific point matching to achieve image registration; automatically segmenting or semi-automatically segmenting the images determined in the same coordinate system to obtain a segmentation map of the multi-source crop remote sensing image; semi-automatically segmenting unplanted bare soil areas and areas with unknown crop growth; for crop areas with known growth, using an improved Grounded‑SAM segmentation model to perform batch fully automatic segmentation processing on multi-source sensor images and output segmentation results; optimizing the segmentation results and finally outputting an optimized segmentation map of the multi-source crop remote sensing image. The advantages are: precise registration, efficient segmentation, multi-sensor compatibility, and automated processing to enhance remote sensing monitoring.

The invention belongs to the technical field of biomedicine, and particularly relates to a method for evaluating the elastic contraction function of lung tissue and application thereof.The method for evaluating the elastic contraction function of the lung tissue comprises the following steps that (1) a semi-automatic segmentation module of ITK-SNAP software is adopted for lung area recognition, and an initial lung segmentation mask is generated; performing 10 voxel morphological expansion on the mask to generate an inspiration sequence mask and an expiration sequence mask; (2) outputting to obtain a shape transformation matrix; the method comprises the following steps: quantitatively analyzing lung elastic contraction characteristics of an IPF patient by using an elastic registration algorithm, and analyzing the correlation between elastic contraction parameters and lung function parameters, dyspnea degree, exercise tolerance, health-related life indexes and the correlation between pulmonary fibrosis degree and pulmonary blood vessel related parameters quantified based on HRCT (High Resolution Computed Tomography); mRI-based lung elastic contraction quantitative analysis is expected to become a novel image marker and is used for noninvasive evaluation of lung tissue elasticity of an IPF patient.

The present invention relates to the field of crop monitoring technology, and in particular to a method for automatic crop segmentation based on multi-source remote sensing images. The method comprises: determining all images in the same coordinate system through geographic registration, point cloud file conversion and ground specific point matching to achieve image registration; performing full-automatic segmentation or semi-automatic segmentation on the images determined in the same coordinate system to obtain a cell segmentation map of the multi-source crop remote sensing image; specifically comprising: semi-automatic segmentation of unplanted bare soil areas and areas with unknown crop growth; automatic segmentation using traditional algorithms for crop areas with known growth; and batch fully automatic segmentation processing of the images segmented in the previous step using an improved Grounded‑SAM segmentation model, outputting the segmentation results. The advantages are: achieving accurate image registration, multi-sensor compatibility, improving accuracy and efficiency through improved algorithm batch segmentation processing, and reducing manual intervention.

Disclosed is a method of computer-based small particle measurement for drug formulation in which digital microscopy images of the small particles used for drug formulation are created by an image sensor and provided to a computer performing a measurement software. The software segments the small particles in the digital microscopy images and calculates properties thereof, according to specific parameter sets. The software samples different candidate parameter sets, applies them automatically for the segmentation and / or calculation process, and shows the results via a display to a user. The user picks the a candidate parameter set with the best results, and the software establishes and trains an internal machine learning model with this user feedback. The software then applies the trained model to reiterate the automatic segmentation and / or calculation and user feedback obtaining process until an optimal parameter set is approved by the user.

The invention discloses a three-dimensional volume data segmentation method and device, and relates to the field of image processing. Through combination of a machine learning model and a traditional algorithm, advantages of deep learning and the traditional algorithm are taken into consideration, a complex deep learning model can be replaced by a simple machine learning model, the method is more portable and easy to deploy and use, a user does not need to label a large number of labels and carry out a large number of training, and the user experience is improved. And the segmentation line of any frame in the tomography three-dimensional volume data can be obtained only by editing one segmentation line on one image frame. Moreover, the user uses a semi-automatic segmentation method during manual segmentation, that is, the user only needs to click several coordinates on an image frame, and then rapid and accurate segmentation of the boundary of the target layer can be completed. The algorithm utilizes the supervision quantity manually input by the user, is equivalent to a weak supervision algorithm, and compared with a traditional algorithm, the algorithm is more accurate, has better generalization ability and can be suitable for tomography three-dimensional body data of various different sources and different conditions.

The present invention relates to the field of crop monitoring technology, and in particular to a method for automatic crop phenotype recognition based on geographic prior information. The method comprises: determining all images in the same coordinate system through geographic registration, point cloud file conversion and ground specific point matching; performing full-automatic segmentation or semi-automatic segmentation on the images determined in the same coordinate system to obtain a cell segmentation map of the multi-source crop remote sensing image; using an improved Grounded‑SAM segmentation model to perform batch full-automatic segmentation processing on the segmented images and output the segmentation results; using an algorithm to extract plant phenotypic parameters, physical parameters and chemical parameters from the segmentation map, integrating them into a unified data framework, standardizing the integrated data, and performing crop classification and recognition. The advantages are: improving the efficiency of crop phenotypic detection, being applicable to multi-source remote sensing data, improving data accuracy and consistency, supporting large-scale data processing, and enhancing crop growth monitoring capabilities.

The application relates to a deep learning-based vascular embolism identification method, which comprises the following steps: obtaining seed points of a to-be-identified abdominal medical image according to an abdominal aorta blood vessel image and a trained first model; performing semi-automatic segmentation on a mesenteric artery region in the image by using a GrowCut algorithm on the seed points; extracting the image of the mesenteric artery region; inputting the image of the mesenteric artery region into a VTK platform; demarcating an arterial blood vessel center line of the image of the mesenteric artery region; inputting the image of the mesenteric artery region with the demarcated arterial blood vessel center line into a second model; and determining whether a thrombus exists in the to-be-identified abdominal medical image and the position of the thrombus. The application can more comprehensively capture influencing factors of acute superior mesenteric artery thrombosis, screen out relevant strong predictors, accurately identify the superior mesenteric artery thrombosis on a CT image, and enhance the prediction ability of the model. The application also relates to an equipment and a storage medium.

The invention relates to the technical field of crop monitoring, in particular to a crop phenotype automatic identification method based on geographic prior information. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching; performing full-automatic segmentation or semi-automatic segmentation on the images determined in the same coordinate system to obtain a cell segmentation map of the multi-source crop remote sensing image; carrying out batch full-automatic segmentation processing on the segmented image by adopting an improved Ground-SAM segmentation large model, and outputting a segmentation result; plant phenotypic parameters, physical parameters and chemical parameters are extracted from the segmented image by using an algorithm and are integrated into a unified data framework, the integrated data are subjected to standardization processing, and crop classification and identification are carried out. The method has the advantages that the crop phenotype detection efficiency is improved, the method is suitable for multi-source remote sensing data, the data precision and consistency are improved, large-scale data processing is supported, and the crop growth monitoring capability is enhanced.

The invention discloses an nnUNet zebra fish juvenile fish whole-brain blood vessel system segmentation method based on autonomous data set training, and relates to the technical field of high-resolution imaging technology, image processing and medical image segmentation. According to the method, high-resolution whole-brain three-dimensional blood vessel image data is obtained by fully utilizing the transparency and fluorescence labeling advantages of a zebra fish living body; and a high-quality segmentation truth value database is constructed through semi-automatic segmentation and manual correction, and an nnU-Net deep learning model is adopted for training, so that three-dimensional automatic segmentation of zebra fish cerebrovascular system signals is realized. According to the method, the automation degree and precision of image segmentation are remarkably improved, and the problems of low efficiency, high manual dependency, poor repeatability and the like of traditional cerebral vessel segmentation are effectively solved. The method is suitable for large-scale high-throughput data processing, can provide an efficient and standardized image processing scheme for zebra fish cerebrovascular development mechanism and cerebrovascular disease model research, and has a wide application prospect.

The invention relates to the technical field of crop monitoring, in particular to an automatic crop segmentation method based on a multi-source remote sensing image. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing full-automatic segmentation or semi-automatic segmentation on the images determined in the same coordinate system to obtain a cell segmentation map of the multi-source crop remote sensing image; the method specifically comprises the following steps: performing semi-automatic segmentation on an unplanted bare soil region and a region of crops with unknown growth vigor; aiming at a crop area with known growth vigor, performing automatic segmentation by adopting a traditional algorithm; and carrying out batch full-automatic segmentation processing on the image segmented in the previous step by adopting an improved Ground-SAM segmentation large model, and outputting a segmentation result. The method has the advantages that accurate image registration and multi-sensor compatibility are realized, the accuracy and efficiency are improved by improving algorithm batch segmentation processing, and manual intervention is reduced.