2 results about "Monosomal karyotype" patented technology

The utility model discloses a novel chromosome karyotype analysis platform which comprises an analysis table, a limiting groove is formed in one side of the analysis table, a limiting block is connected in the limiting groove in a sliding mode, a first connecting rod is arranged on one side of the limiting block, a first glass slide is arranged at one end of the first connecting rod, and a second glass slide is arranged at the other end of the first connecting rod. Two second connecting rods are arranged on one side of the analysis table, a second glass slide plate is arranged between the two second connecting rods, rotating shafts are arranged on the two sides of the second glass slide plate, and the rotating shafts are rotationally connected with the second connecting rods; through the sliding / rotating design of the limiting groove and the second connecting rod, the glass slide is supported to be flexibly adjusted, the chromosome banding is conveniently observed from multiple angles, the environment is kept clean in combination with the fresh air assembly of the double fans, and the sample pollution risk is reduced.

This application provides a method and system for intelligent chromosome karyotype segmentation that integrates deep learning. The method involves: enhancing chromosome microscopic images; performing region segmentation based on the enhanced chromosome images to obtain multiple chromosome region sub-images; for any chromosome region sub-image, extracting the chromosome pixel area and band count corresponding to that sub-image, and determining the structural coverage index of the sub-image based on these values; comparing the structural coverage index with a preset chromosome banding distribution model to perform anomaly screening and identify candidate chromosome adhesion regions; and segmenting the candidate adhesion regions using a deep learning segmentation model to obtain the corresponding chromosome karyotype segmentation image. This application can screen and identify candidate adhesion regions based on the chromosome skeleton banding density, improving the accuracy of chromosome image segmentation.