Mechanical arm thoracocentesis path planning system and method based on dynamic adaptive ant colony optimization algorithm

Through the robotic arm thoracic puncture path planning system based on the dynamic adaptive ant colony optimization algorithm, combined with image segmentation technology and ant colony algorithm, the problem of inaccurate tumor and blood vessel identification in the existing technology is solved, accurate and safe puncture path planning is achieved, and surgical risks are reduced.

CN120635117APending Publication Date: 2025-09-12SHANDONG UNIV
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Patent Information

Application Number
CN202510792377.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-13
Publication Date
2025-09-12

AI Technical Summary

Technical Problem

Existing thoracentesis path planning methods fail to effectively combine medical imaging for accurate identification of tumors and blood vessels, resulting in inaccurate path selection, affecting treatment outcomes, and posing puncture risks.

Method used

A robotic arm thoracotomy path planning system based on a dynamic adaptive ant colony optimization algorithm is used. Combining image segmentation technology and an improved ant colony algorithm, the DenseNet network is used to automatically segment tumors, blood vessels, trachea, and bones. A dynamic adaptive ant colony optimization algorithm is used to plan a straight path to avoid important structures.

Benefits of technology

It enables accurate and safe planning of the path during thoracentesis, avoiding important structures such as blood vessels, improving the accuracy and safety of puncture, and reducing surgical risks.

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Abstract

The invention relates to a mechanical arm thoracocentesis path planning system and method based on a dynamic adaptive ant colony optimization algorithm, and belongs to the technical field of path planning. The system comprises a data preprocessing module, an image segmentation module, a path planning module and a visualization module; the data preprocessing module is used for performing denoising and contrast enhancement on the chest CT image so as to improve the image quality and facilitate subsequent image segmentation and analysis; the image segmentation module is used for automatically segmenting tumor, blood vessel, trachea and bone areas through DenseNet and providing data support for path planning; the path planning module is used for selecting an optimal path or an approximate global optimal path by using a dynamic adaptive ant colony optimization algorithm; and the visualization module is used for presenting the optimal path or the approximate global optimal path on the original chest CT image. The system uses an image segmentation technology and an improved ant colony algorithm to provide an optimal puncture path for puncture.
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