Deep learning time sequence alignment method and system for multi-view asynchronous sequence image

By using EfficientNet-B0 and the soft dynamic temporal warping alignment loss function, the problem of specialized design for temporal alignment of multi-view asynchronous sequence images is solved, achieving high-precision alignment under weak supervision and improving the reliability of cardiovascular image analysis.

CN121707987APending Publication Date: 2026-03-20HOHAI UNIV
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Patent Information

Application Number
CN202511928905.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-19
Publication Date
2026-03-20

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively handle the temporal alignment of multi-view asynchronous sequence images, particularly in terms of cardiac motion phase consistency and dynamic temporal regularization between sequences. They lack specialized design and rely on high-quality labeled data and stable imaging conditions.

Method used

EfficientNet-B0 is used as the backbone network for feature extraction. Combined with the soft dynamic time warping alignment loss function, the temporal alignment of multi-view asynchronous sequence images is achieved through end-to-end training. The non-differentiable operations in DTW are replaced with differentiable operations by using the softening parameter γ. Combined with bandwidth constraint encoding temporal smoothness, feature extraction and alignment are performed.

Benefits of technology

High-precision temporal alignment of multi-view asynchronous sequence images was achieved under weak supervision, overcoming the dependence on high-quality labeled data and stable imaging conditions, and improving the reliability of cardiovascular image analysis.

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Abstract

The invention discloses a deep learning time sequence alignment method and system for a multi-view asynchronous sequence image, and the method comprises the following steps: carrying out the image screening of a multi-view asynchronous collected angiography sequence, and constructing a short sequence composed of three frames of continuous images; the method comprises the following steps of: extracting a backbone network by adopting an OfficientNet-B0 as a feature extraction backbone network, removing a classification head of the feature extraction backbone network, replacing the classification head with identical mapping, outputting convolutional features, and performing network optimization in combination with a soft dynamic regularization alignment loss function (soft DTW); extracting a feature vector describing the heart cycle progress through a network; calculating the cosine similarity of the feature vectors among different view angles, constructing a similarity matrix, and searching an optimal alignment path in the similarity matrix by using a soft dynamic time warping algorithm; and outputting a time sequence alignment frame and a corresponding similarity score according to the optimal alignment path. According to the method, the multi-view asynchronous sequence image can be effectively processed, time sequence alignment is realized, and a reliable alignment basis is provided for cardiovascular image analysis.
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