Development of microcatheter tube extrusion angle estimation system using convolutional neural network segmentation.

This study presents a deep learning-based monitoring system for estimating extrusion angles in the manufacturing process of microcatheter tubes. Given the critical nature of these tubes, which are directly inserted into the human body, strict quality control is imperative. To mitigate potential quality variations stemming from operator actions, a system utilizing a convolutional neural network to precisely measure the extrusion angle-a parameter with profound implications for tube quality-is developed. Until now, there has been no method to estimate the extrusion angle of resin being extruded in real-time. In this study, for the first time, a method using deep learning to estimate the angle was proposed. This innovative system comprises two RGB cameras capturing both front and side perspectives. The acquired images undergo segmentation via a meticulously trained convolutional neural network. Subsequently, the extrusion angle is accurately estimated through the application of principal component analysis on the segmented image. The usefulness of the proposed system was rigorously confirmed through comprehensive validation measures, including mean intersection over union (mIoU), mean absolute angle error (MAE), and inference time, using a real-world dataset. The attained metrics, with an mIoU of 0.8848, MAE of 0.5968, and an inference time of 0.0546, unequivocally affirm the system's suitability for enhancing the catheter tube extrusion process.