Image segmentation method, gestational week prediction method and diagnostic report determination method and device

By using the layer adaptive feature gating module and bottleneck domain adaptive feature calibration module in the U-shaped image segmentation network, the problem of multi-faceted and multi-structure collaborative segmentation is solved, and high-precision fetal anatomical structure segmentation and gestational age prediction are achieved.

CN121033408APending Publication Date: 2025-11-28WUHAN UNIV
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Patent Information

Application Number
CN202510915847.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-03
Publication Date
2025-11-28

AI Technical Summary

Technical Problem

Existing model-based ultrasound image segmentation methods are insufficient to meet the requirements of multi-plane and multi-structure collaborative segmentation, and their application is particularly limited in gestational age prediction.

Method used

A U-shaped image segmentation network is adopted, which combines a layer adaptive feature gating module and a bottleneck domain adaptive feature calibration module. The expressive ability of key anatomical structures is enhanced through channel attention and spatial attention mechanisms, and shallow spatial details are used to guide deep semantic localization, thereby improving the robustness of multi-section organ segmentation.

Benefits of technology

It achieves high-precision segmentation and localization of organs in multiple planes, improves the accuracy and segmentation precision of gestational age prediction, supports the end-to-end gestational age prediction process, and reduces the impact of human intervention.

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Abstract

The invention provides an image segmentation method, a gestational week prediction method and a diagnosis report determination method and device, and belongs to the field of medical artificial intelligence. The image segmentation method comprises the steps that a U-shaped image segmentation network is constructed, and the U-shaped image segmentation network comprises an encoder and a decoder; wherein the encoder and the decoder are connected through a jump connection layer; a self-adaptive feature gating module of each jump connection layer embedded layer of the U-shaped image segmentation network is used for strengthening the expression ability of a key anatomical structure and filtering irrelevant information; a bottleneck layer of the U-shaped image segmentation network is embedded into a bottleneck domain adaptive feature calibration module which is used for guiding deep semantic positioning by utilizing shallow space details and improving multi-section organ segmentation robustness; training the U-shaped image segmentation network by adopting the training set to obtain an image segmentation model; and inputting a to-be-segmented image into the image segmentation model to obtain an image segmentation result. According to the method, standardized, multi-section and multi-structure collaborative segmentation of the image can be realized.
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