8 results about "Image Quantification" patented technology

This invention discloses a method and system for quantitatively assessing the degree of fibrosis in kidney pathological sections, belonging to the field of medical image processing technology. The method includes: performing color normalization and adaptive contrast enhancement preprocessing on Masson stained section images; classifying pixel-level tissue components using a multi-component semantic segmentation network containing a channel-space dual-path attention module; calculating the fibrosis area ratio and outputting a CI score based on the Banff grading standard; generating a fibrosis spatial distribution heatmap; and calculating the annual fibrosis progression rate through longitudinal follow-up comparison.

The application discloses a method for multi-modal large model inference acceleration, and belongs to the technical field of artificial intelligence, aiming to solve the technical problems of long time consumption in the pre-filling stage caused by large image quantization loss and too many image coding tokens during the inference of a multi-modal large model. The method first extracts the overall features of an image through a mask auto-encoding network, then acquires the candidate regions of the image by using a detection network and completes the alignment and extraction of the regional features, and obtains the image sequence feature vector after the overall features and the local features are fused; meanwhile, the input text is segmented and embedded to obtain the text word vector, and the two types of vectors are spliced and fused according to the sequence dimension to input a large language decoding model to complete decoding. The application can greatly reduce the image token calculation amount, significantly reduce the time consumption in the inference pre-filling stage under the premise of ensuring the inference effect, and improve the overall inference efficiency of the multi-modal large model.

The invention discloses a method for observing the shape and number of synapses in a dentate gyrus region based on image analysis, and relates to the technical field of neurobiology. The method comprises the following steps: S1, tissue treatment: after an experimental animal is anesthetized, pouring a buffer solution and a stationary solution into the heart in stages, separating hippocampus tissue, trimming and fixing, pre-cooling, slicing and preserving at low temperature; s2, dyeing and marking: performing targeted marking on the dentate gyrus area of the sea horse by adopting DiI particles through a fine tungsten steel through needle, and preserving at low temperature after incubation; s3, fixing and mounting: fixing for the second time, and mounting with an anti-fluorescence quenching agent after DAPI nuclear dyeing; s4, confocal imaging is carried out, exclusive parameter layer scanning is set, and qualified dendritic spine images are screened; and image quantitative analysis can be further carried out, and accurate quantification is realized according to classification standards. The method is standard in operation, greatly improves sample quality and experimental result credibility, optimizes experimental efficiency and controllability and reduces technical threshold by means of targeted marking and standardized processes, and is suitable for relevant basis and application research of the hippocampal synapses.

The application belongs to the field of display module residual image quantitative test, and relates to a display module residual image quantitative test device, which comprises a lower jig, an upper jig b provided above the lower jig, and an upper jig a fixedly connected to one side of the upper jig b; a shielding assembly provided between the lower jig and the upper jig b, the shielding assembly comprising a through hole b, a partition grid a and an optical test probe; and a switching assembly provided on the top of the lower jig. Through the arrangement of the shielding assembly, the upper jig a or the upper jig b can cover the entire display module main screen area, and only the through hole a or the through hole b position is reserved for brightness measurement, external light interference is effectively shielded, and it is ensured that the measurement data reflects the true brightness value of the display module main body itself. Through the arrangement of the switching assembly, the switching of the upper jig a and the upper jig b can facilitate the test of the black-white junction center position and improve the detection comprehensiveness.

The invention discloses a WSI image quantitative evaluation system based on cross-scale feature collaboration and multi-dimensional quality factor fusion, belongs to the technical field of digital pathological image analysis, and aims at solving the problems that an existing evaluation method is single in dimension and insufficient in robustness and efficiency. The system obtains a WSI image through scanning, randomly samples a tissue area under a basic amplification factor, screens out an unclear area by adopting a definition detection method, and feeds back a re-scanning instruction. And for the clear area, synchronously extracting image slices with different objective lens magnifications under the same hierarchy of the pyramid structure, and constructing a multi-magnification slice data set. And carrying out cell identification and counting based on high-magnification slices, carrying out bubble area detection and staining defect identification based on low-magnification slices, and respectively generating cell number, slice integrity and staining quality sub-item scores. And finally, performing weighted fusion on each item score to obtain a slice comprehensive quality score, and realizing objective quantitative evaluation on the WSI image quality.

The application discloses a CT image quantitative analysis system based on machine learning and relates to the technical field of image processing. The application comprises an information acquisition module for acquiring CT image data and analysis requirements; and an information processing module for performing specification uniform processing on the CT image data based on the CT image data through a specification processing method to obtain to-be-analyzed image data. The application can cross-verify and optimize multiple sub-information obtained from the to-be-analyzed image data through preset verification quantity and a subsequent cross-verification module, so as to improve the accuracy of characteristic region name and quantity information reflecting the to-be-analyzed image data, and further improve the accuracy of subsequent model processing. A single characteristic recognition model can analyze only a single characteristic region, so that the efficiency of characteristic recognition analysis can be improved, and multiple different recognition analysis requirements can be processed, multi-task processing is realized, and the efficiency of information processing is improved, so as to meet the increasing demand for precise diagnosis in clinical practice.

This invention provides a method and electronic device for generating periodontitis reports based on dual-energy CBCT. The method includes: acquiring high- and low-energy dual-energy CBCT projection data, an intraoral 3D scanning model, and structured electronic medical record data of the patient; reconstructing high- and low-energy CBCT volumetric images; obtaining image quantification features and quantification indicators; aligning and fusing the image quantification features with clinical features in the intraoral 3D scanning model and electronic medical record data at the tooth position level to obtain staging results; calculating a consistency coefficient; when the consistency coefficient is lower than a preset threshold, triggering a conflict alarm and automatically adjusting the evidence weight in the final staging decision to obtain conflict detection results; and automatically generating a structured diagnostic report based on the quantification indicators, staging results, and conflict detection results. This aims to improve diagnostic accuracy in complex oral environments.

The application discloses a method for distinguishing pores and bubbles in ultra-stable foam based on FC and sphericity analysis, belongs to the field of solid-liquid separation of flotation clean coal, and comprises the following steps: obtaining a three-dimensional gray image of the foam through Micro-CT, performing three-dimensional reconstruction and extracting a gap structure according to a filtered back projection algorithm; determining a representative sub-volume based on porosity change to ensure statistical reliability; calculating geometric characteristic parameters of the gap and performing morphological classification by adopting a Feret Caliper method; determining a distinguishing threshold TH in combination with a relationship between sphericity and bubble volume fraction; then, realizing automatic identification and distinction of the bubbles and the pores according to the threshold; and finally, verifying the result by using an optical microscope. The method realizes non-destructive identification and quantitative analysis of the internal structure of the ultra-stable foam, has high precision and repeatability, and provides a reliable image quantitative means for optimization of a coal dewatering process.