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Related Experiment Video

Updated: Jul 12, 2025

Construction and Implantation of a Microinfusion System for Sustained Delivery of Neuroactive Agents.
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Development of microcatheter tube extrusion angle estimation system using convolutional neural network segmentation.

Seung Hyun Jeong1, Sang Heon Lee2, Hong-In Won3

  • 1School of Mechatronics Engineering, Korea University of Technology and Education, Cheonan-si, 31253, Republic of Korea.

Scientific Reports
|October 27, 2023
PubMed
Summary

This study introduces a novel deep learning system for real-time extrusion angle estimation in microcatheter manufacturing. The system ensures quality control for medical devices by accurately measuring extrusion angles, improving safety and consistency.

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Last Updated: Jul 12, 2025

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Area of Science:

  • Manufacturing Engineering
  • Medical Device Technology
  • Artificial Intelligence in Manufacturing

Background:

  • Microcatheter tubes require stringent quality control due to their medical application.
  • Operator variability can lead to inconsistencies in the extrusion angle, impacting microcatheter quality.
  • Real-time estimation of extrusion angles during resin extrusion has been a significant challenge.

Purpose of the Study:

  • To develop and validate a deep learning-based system for real-time extrusion angle estimation in microcatheter manufacturing.
  • To enhance quality control and reduce variability in the microcatheter extrusion process.
  • To establish a novel method for real-time extrusion angle measurement.

Main Methods:

  • A system employing two RGB cameras for front and side views was designed.
  • A convolutional neural network (CNN) was trained for image segmentation of the extruded resin.
  • Principal Component Analysis (PCA) was applied to segmented images for accurate extrusion angle estimation.

Main Results:

  • The system achieved a mean intersection over union (mIoU) of 0.8848 for segmentation accuracy.
  • The mean absolute angle error (MAE) was 0.5968, demonstrating high precision.
  • An inference time of 0.0546 seconds confirmed the system's real-time capability.

Conclusions:

  • The proposed deep learning system effectively estimates extrusion angles in real-time.
  • The validated system is suitable for improving quality control in microcatheter tube manufacturing.
  • This represents the first real-time deep learning method for estimating extrusion angles in this process.