A limb volume correction method based on one-dimensional convolutional neural network

By using a limb volume correction method based on a one-dimensional convolutional neural network, the problem of volume measurement error in infrared grating measurement technology was solved, enabling accurate diagnosis of lymphedema and improving the accuracy of volume measurement and early detection capability.

CN121730802BActive Publication Date: 2026-07-24ZHEJIANG UNIV
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Patent Information

Application Number
CN202610231208.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-02-27
Publication Date
2026-07-24
Estimated Expiration
2046-02-27

AI Technical Summary

Technical Problem

Existing infrared grating measurement technology suffers from systematic errors in volume measurement due to sensor physical blind spots and geometric simplification algorithms. This makes it difficult to ensure both the accuracy of absolute volume measurement and the identification of local morphological changes while maintaining convenience, thus failing to effectively support the accurate diagnosis of early lymphedema.

Method used

A limb volume correction method based on a one-dimensional convolutional neural network is adopted. By constructing a dynamic feature matrix and training dataset, the original length and width data sequences are collected using an infrared measurement grating. A one-dimensional convolutional neural network regression model is trained to correct the initial measured volume data. The volume correction is then performed by combining the elliptical cylinder accumulation algorithm.

Benefits of technology

It achieves error compensation for sensor physical blind spots and geometric simplification algorithms, improves the accuracy of volume measurement and the ability to identify local morphological changes, reduces the risk of false positive diagnosis, and enhances the detection sensitivity and diagnostic reliability of early lymphedema.

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Abstract

The application discloses a kind of limb volume correction methods based on one-dimensional convolutional neural network, it is related to medical technical field.The method includes: for multiple training sample limbs, the original length-width data sequence of each sample and reference volume are collected, dynamic feature matrix is established and training data set is constructed;With dynamic feature matrix as input, with its corresponding reference volume as supervision label, train one-dimensional convolutional neural network regression model;For the limb to be corrected, the original length-width data sequence thereof is collected and dynamic feature matrix is established;Finally, input into the model trained to obtain predicted volume, and the initial measurement volume data of the limb is corrected.The application accurately captures the local morphological change trend of the limb by dynamic characteristics, compensates the systematic error and local protrusion misjudgment of prior art by means of neural network end-to-end learning, effectively improves the volume measurement accuracy, reduces the false positive risk, and is suitable for clinical early lymphedema monitoring.
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