Lung canceration region segmentation and classification detection system

Abstract

The invention discloses a lung canceration region segmentation and classification detection system. Firstly, carrying out preliminary pre-segmentation by using a pre-segmentation model; screening pre-partitioned regions, carrying out gradient calculation on the pre-segmentation area; obtaining a main concentrated part of the misdiagnosis area, that is, tissue region edge, for these edge regions, segmenting the canceration area accurately by using the high-precision fine segmentation model, wherein only the edge area is involved in the fine segmentation algorithm, so that compared with the existing method for finely segmenting the whole tissue area, the calculated amount is remarkably reduced, and the balance between the rapidity and the accuracy of detection can be realized. According tothe method, the rapidity of the pre-segmentation model and the accuracy of the fine segmentation model are combined, the canceration region segmentation algorithm capable of meeting the actual application level can be achieved, the algorithm can be applied to the actual social production process, the detection workload is effectively reduced, and the diagnosis speed is increased.

Description

technical field [0001] The invention relates to the technical fields of biomedical engineering, medical imaging and artificial intelligence, in particular to a lung cancer region segmentation and classification detection system. Background technique [0002] Lung cancer is one of the cancers with the highest mortality rate. The diagnosis of cancer first needs to locate the cancerous area and then identify the type. Among them, histopathological image analysis can be used as the gold standard for the diagnosis of lung cancer. [0003] Classification and extent assessment of cancer types are crucial for targeted therapy. In clinical practice, experienced pathologists identify cancer by scanning H&E-stained tissue slides into Whole Slide Image (WSI) and observing the diagnosis. Due to the large scale of the image data, normal areas and Cancerous areas are relatively similar, therefore, it is a time-consuming and laborious work, for example, it takes about 15 minutes to half a...

AI Technical Summary

Problems solved by technology

These methods have certain limitations. After the image is divided into slices and then the slices are classified to achieve segmentation, the segmentation accuracy often does not meet the requirements and the calculation time is too long; after the image is divided into slices, the slice The method of segmenting blocks has high requirements for computing resources and large edge errors; the method of directly segmenting low-resolution images after scaling the image resolution is too high in misdiagnosis rate

Images