Segmentation method of pathological section unconventional cells based on multi-scale hybrid segmentation model

Abstract

The invention discloses a segmentation method of pathological section unconventional cells based on a multi-scale hybrid segmentation model. The method comprises steps that positive and negative samples are respectively scaled to low-resolution, medium-resolution and high-resolution images, the full convolutional network algorithm is utilized for training to acquire a convergent low-resolution segmentation model, a medium-resolution segmentation model and a high-resolution model; a multi-scale hybrid segmentation model is acquired through fusion by a model integration method; after the effective discriminating area of a new pathological section is processed through utilizing the data enhancement method during testing, the processed effective discriminating area is inputted to the multi-scale hybrid segmentation model, the probability of each pixel in the effective segmentation area is outputted, pixels with probability values greater than the threshold t are taken as abnormal cell pixels and are recorded as 1, remaining pixels are taken as normal cell pixels and are recorded as 0, binary images predicted by the multi-scale hybrid segmentation model are acquired, and post-processingon the binary images is carried out to acquire the final segmentation result. The method is advantaged in that high precision is realized, and a Dice value is above 0.869.

Description

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AI Technical Summary

Benefits of technology

This patented technique described in this patents allows researchers to easily identify specific areas within image samples based on their size or shape without having access to specialized equipment like histology labs. It also improves upon existing techniques while reducing labor costs compared to current technologies. Overall, it provides technical benefits over conventional approaches.

Problems solved by technology

The technical problem addressed in this patents relates to improving the performance of semiconductor devices that can accurately identify specific areas within images without being affected by noise from surrounding background structures like walls or objects during scanning processes. This requires accurate and efficient techniques for analyzing complex datasets containing both grayscale and color feature maps obtained with different sensors.

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