An image segmentation method and medium for densely populated regions of living cells

By generating predictive auxiliary modality images through a feature extraction network and optimizing dynamic weights, the undersegmentation problem of single-modal input in the segmentation of dense live cell regions is solved, achieving high-precision and low-cost live cell image segmentation, avoiding model degradation and high annotation costs.

CN122090447APending Publication Date: 2026-05-26SAIL SPACE (SUZHOU) INTELLIGENT TECHNOLOGY CO LTD
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SAIL SPACE (SUZHOU) INTELLIGENT TECHNOLOGY CO LTD
Filing Date
2026-03-23
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing deep learning models suffer from problems in segmenting densely populated regions of live cells, such as undersegmentation under single-modal input, high cost of fine-tuning large models that easily leads to a decline in generalization ability, difficulty in obtaining paired multimodal auxiliary information during the actual inference stage, and existing pseudomodal prediction and splicing schemes are prone to feature attention shift and performance degradation.

Method used

By acquiring a single-modal main image and pairs of real auxiliary modal images, a feature extraction network is used to generate predicted auxiliary modal images. Through dynamic weight optimization and loss function adjustment, combined with a pre-trained large segmentation model, image segmentation is performed, achieving high-quality auxiliary modality generation and feature fusion under single modality.

Benefits of technology

Without relying on real multimodal input, it improves the segmentation recall and accuracy of densely populated live cell regions, avoids the high cost of manual annotation and the risk of model degradation, and achieves high-precision, low-cost single-modal live cell instance segmentation.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122090447A_ABST
    Figure CN122090447A_ABST
Patent Text Reader

Abstract

This invention discloses an image segmentation method and medium for densely populated live cell regions. The method includes acquiring multiple first data pairs and preprocessing each first data pair. Each first data pair includes a single-modal master image of a live cell region and a paired real auxiliary modality image. A feature extraction network is trained using the preprocessed first data pairs, and the feature extraction network outputs a predicted auxiliary modality image corresponding to the single-modal master image. The preprocessed first data pairs and the predicted auxiliary modality image are input into a pre-trained segmentation model, and the dynamic weights of the predicted auxiliary modality image are adjusted to optimize a second loss function to obtain optimal weight parameters. The single-modal master image of the live cell image to be segmented is input into the trained feature extraction network, and combined with the optimal weight parameters, it is input into the pre-trained segmentation model to obtain the segmentation result. This method is particularly effective for accurate segmentation in densely populated regions.
Need to check novelty before this filing date? Find Prior Art