Automatic focusing method and system of cell slide scanner

By using multimodal information fusion and deep learning to predict the focus position, the problem of long autofocus time and poor adaptability of traditional cell slide scanners is solved, and an efficient and accurate autofocus process is achieved.

CN121933422AActive Publication Date: 2026-04-28JIANGSU MICROCONTROL BIOTECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610404952.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-31
Publication Date
2026-04-28
Estimated Expiration
2046-03-31

AI Technical Summary

Technical Problem

Traditional cell slide scanners use time-consuming autofocusing methods, are poorly adapted to complex samples with low contrast, high background fluorescence, and scratches, and the focus parameter settings rely on subjective experience.

Method used

By combining multimodal information fusion and deep learning prediction to generate a focus strategy map, the optimal focus point is determined through conditional verification and adjustment. Using transmission brightfield images, fluorescence distribution images, and physical height sensing data, a pre-trained focus prediction model is applied to directly predict the focus position.

Benefits of technology

This technology enables high efficiency and high precision in automatic focusing of cell slide scanners, shortens scanning time, improves focusing success rate and robustness for complex samples, and ensures image quality.

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Abstract

The invention discloses an automatic focusing method and system of a cell slide scanner, and relates to the related field of optical equipment focusing technology.The method comprises the steps that a slide is previewed and scanned, a multi-mode preview image and physical height sensing data are synchronously collected and preprocessed, and then the pre-processed image is obtained; the multi-modal preview image and the corresponding height data are input into a pre-training focus prediction model, a focusing strategy graph aligned with a slide coordinate space is output, and the graph comprises the predicted optimal focus offset, the focusing function recommendation identifier, the focus verification confidence coefficient and the search radius of each view point; according to the focusing strategy graph, a final focus is determined through feedforward positioning, limited verification and conditional adjustment, a scanner objective table is controlled to carry out formal scanning at the final focus position, and a complete scanning image of the cell slide is collected. The problems that a traditional focusing method is long in time consumption and poor in adaptability to complex samples with low contrast, high background fluorescence and scratch pollution are solved, and the adaptive capacity of the focusing process is improved.
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