Method and apparatus for analyzing midbrain hyperechoic area based on neural network

By using a neural network-based approach, FasterRcnn and deeplab v3+ neural networks are used to automatically identify and segment the strong echo regions in the midbrain, solving the problems of inaccurate and cumbersome assessment in existing technologies, and achieving efficient and accurate assessment of Parkinson's disease.

CN116363062BActive Publication Date: 2026-07-24SUZHOU LINATECH MEDICAL SCI & TECH CO LTD
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Patent Information

Application Number
CN202310116450.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-02-15
Publication Date
2026-07-24
Estimated Expiration
2043-02-15

AI Technical Summary

Technical Problem

In existing technologies, diagnostic methods for Parkinson's disease, such as transcranial substantia nigra ultrasound and electron emission computed tomography, suffer from problems such as large errors or high costs, making it difficult to accurately assess the area of ​​hyperechoic regions in the midbrain, resulting in an insufficiently objective and convenient assessment of the condition.

Method used

A neural network-based approach was adopted, using Faster R-CNN and DeepLab v3+ neural networks to automatically identify and segment the hyperechoic regions of the midbrain. Combined with feature extraction and candidate box regression techniques, quantitative analysis of the hyperechoic regions of the midbrain was achieved.

Benefits of technology

It improves the accuracy of quantitative analysis of the area of ​​strong echogenic regions in the midbrain, simplifies the operation process, and enables automatic and objective assessment of the progression of Parkinson's disease.

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Abstract

The application discloses a kind of neural network-based midbrain strong echo area analysis method and equipment, comprising the following steps: collecting transcranial nigral ultrasound image;The collected ultrasound image is preprocessed;Training detection neural network trains;After being detected by detection neural network, the ultrasound image with midbrain strong echo region is preprocessed again;Train segmentation neural network;The ultrasound image to be detected is input into trained detection neural network and segmentation neural network, and the segmentation of midbrain strong echo region is obtained;Quantitative analysis of the area of midbrain strong echo region.The application is based on transcranial nigral ultrasound, combined with neural network automatic identification and evaluation of the boundary of midbrain strong echo, quantitative analysis of the area of midbrain strong echo region, higher accuracy than traditional manual measurement estimation, which can effectively realize automatic evaluation of the progression of Parkinson's disease.
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Citation Information

Patent Citations

  • Transcranial ultrasound nigra high echo intensity quantitative analysis method

    CN113538380A