Fine adjustment method for cell segmentation

Abstract

The invention discloses a cell segmentation fine tuning method, which comprises the following steps: S1, a pre-training stage: forming a pre-training set by taking known cell image data of different styles as pre-training data, and performing model training by utilizing the training set to obtain a pre-training model; s2, a fine tuning stage: marking at least 8 cell images from the to-be-segmented cell images of the new style as a shot data set, and taking the remaining cell images as a query data set; performing fine tuning and retraining on the pre-training model obtained in the step S1 by utilizing a shot data set to obtain a training model; and S3, an inference stage: inferring and segmenting the cell image in the query data set in the step S2 by using the training model obtained in the step S2. Compared with the prior art, the method has the advantages that cell segmentation of cell images of different styles can be realized, the application scene of a universal algorithm is widened, and the segmentation performance of the universal algorithm is improved.

Description

technical field [0001] The invention relates to the technical fields of cell imaging, instance segmentation and model fine-tuning, in particular to a cell segmentation fine-tuning method. Background technique [0002] Single cell segmentation technology is currently widely used in the fields of cell counting, spatial transcriptomics, high-throughput drug screening, and tumor metastasis detection. However, due to the lack of robust and easy-to-use cell segmentation algorithms, the method of average analysis is still the most commonly used method, which not only leads to information loss, but also may affect downstream analysis and misinterpret the relationship between features. Therefore, it is of great research value to develop a robust single-cell segmentation algorithm with excellent performance. [0003] However, since different cell types, microscopic imaging equipment, processing methods, imaging modes, and staining methods can produce various styles of cell images, it...

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Problems solved by technology

However, these studies have not kept up with the pace of general algorithm research, and these studies are usually limited to cell nucleus images, and there are a large number of other examples such as nematodes that have segmentation needs

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