Cell image semantic segmentation method fusing image segmentation and classification

Abstract

The invention relates to a cell image semantic segmentation method fusing image segmentation and classification. The method comprises: preprocessing cell image data and then processing the cell imagedata through a bilinear fine-grained classification neural network, an OSTU algorithm and a filling algorithm, and then respectively obtaining a cell classification model and a cell segmentation map;and predicting a foreground connected region of the cell segmentation map by the cell classification model, assigning a prediction result to the connected region so as to obtain a region-by-region classification result, and finally obtaining a semantic segmentation result of the cell test image in combination with a background region obtained by segmentation. A traditional threshold method and a deep learning method are fused to achieve accurate semantic segmentation of the cell image. Compared with a traditional cell image segmentation method, the cell image semantic segmentation method is advantageous in that semantic information of cells can be obtained, the semantic information is of a pixel-by-pixel semantic category, and the method can be applied to identification and isolation of cell contamination.

Description

technical field [0001] The invention relates to the field of cell image processing, and more specifically, to a cell image semantic segmentation method that combines image segmentation and classification. Background technique [0002] Currently, cell semantic segmentation methods include threshold-based segmentation methods and deep learning-based segmentation methods. The threshold-based segmentation method is relatively simple, but the selection of the threshold greatly affects the effect of image segmentation. It only considers the gray value of the pixel itself, and does not consider the spatial distribution of the image, so the segmentation result is very sensitive to noise. resulting in low accuracy. The segmentation method based on deep learning requires a large amount of labeled data as the training samples of the fully convolutional network, especially for semantic segmentation tasks, pixel-level semantic labels are required, which is very difficult to obtain. Mor...

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Problems solved by technology

[0003] In order to overcome the problems of low accuracy of cell image semantic segmentation in the prior art and high cost of fully convolutional network training, the present invention provides a cell image semantic segmentation method that int

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