Image-based mandrel detection during stent production in an industrial environment

Abstract: Of the 985,572 deaths in Germany in 2020, 121,725 people died due to coronary heart disease (CHD). Therefore, CHD is the most common single cause of death in Germany. It is caused by a narrowing of the coronary vessels. This narrowing will lead to an insufficient supply of oxygen and nutrients to the heart muscle, causing a heart attack, heart failure or cardiac arrhythmia. A possible treatment for CHD is the implantation of a stent, which will widen the narrowed vessel. This widening will restore the oxygen and nutrient supply. Being a minimally invasive technique, 298.557 stent implantations were performed in Germany in 2020, accounting for about 88 % of all related interventions. According to the German fee-per-case system, the costs for a single stent range from €54.80 to €1,189.69. Combining the number of implantations with the costs per stent, the resulting financial burden on German health services is imminent. One reason for these high costs is the absence of an automated inspection and correction system during stent production using a maypole braider. Following this argumentation, an automated system for detecting and correcting geometry errors in stents during their production is desirable.To detect errors in a stent during the production process, it is necessary to measure its geometry. This requires knowledge about its position within the image. Since the stent is braided using a maypole braider, locating the stent is equivalent to locating the mandrel. This paper proposes a concept to measure the mandrels’ position during production based on camera images. It differentiates between and handles cylindrical stents as well as curved ones. Also, it compensates for the movements of both the camera and the mandrel in the x and z planes. Movements in the y-plane can be neglected. Additionally, methods to measure a cylindrical mandrel are evaluated, including Canny Edge Detection, the Hough transform, k-means clustering, and a watershed algorithm. In addition, four convolutional neural networks and two object detection models were tested. The lowest mean squared error of 9.04 was achieved using the YoloV10 object detector (mean MSE: 9.04, median MSE: 9.57, mean MAE: 11.5, median MAE: 7.65, and execution time per image: 846.9 ms). The fastest approach with an execution time of 53.06 ms is based on the Canny operator to detect lines as well as a threshold on the images’ histogram to find the position of the mandrels’ borders (mean MSE: 55.89, median MSE: 21.0, mean MAE: 70.51, median MAE: 16.12, and execution time per image: 53 ms). The images being used to train, evaluate, and test all methods were recorded using a maypole braider in an industrial environment.Parts of this work have been developed in the project Stents4Tomorrow. Stents4Tomorrow (reference number: 02P18C022) is partly funded by the German Federal Ministry of Education and Research (BMBF) within the research program ProMed.

Keywords: Stent, Computer Vision, Object Detection, AI, Classical Machine Vision

DOI: 10.54941/ahfe1005933

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