Implicit dimension measurement for automated cross-sectional inspection of multi-lumen medical catheters

Ensuring the cross-sectional shape integrity of medical catheters is a necessity for their safe and effective clinical functionality. Although visual inspection technologies have advanced rapidly, automated inspection of catheter tubes remains challenging due to the complex, deformable structures resulting from extrusion processes and the inherent properties of the materials. In this study, we introduce the Implicit Dimension Measurement (IDiM) framework, which combines rule-based expertise with a data-driven Endpoint Alignment Model (EAM). By parameterizing cross-sectional dimensions using carefully defined endpoints and reference points, IDiM robustly infers key geometric features—even under moderate deformations. We validate its measurement accuracy on multi-lumen catheters (2-, 3-, and 4-lumen) through a high-resolution imaging setup deployed on an actual production line. Experimental results demonstrate measurement precision within five pixels of inter-annotator deviation, comparable to that of human inspectors, along with reliable detection of severe deformation cases via an anomaly detection approach. These findings highlight the practical feasibility of IDiM for high-fidelity shape inspection in medical manufacturing and suggest its broader applicability to other industries requiring precise dimensional verification. • Proposes IDiM for automated cross-sectional inspection of medical catheter. • Introduces EAM to mitigate ambiguities from symmetry and deformation. • Validated through production systems and microscale imaging experiments. • Achieves human-level accuracy with precision below 50 μ m .

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Publication Details

Journal
Journal of Industrial Information Integration
Published
2025-10-25
DOI
https://doi.org/10.1016/j.jii.2025.100979
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
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article

Implicit dimension measurement for automated cross-sectional inspection of multi-lumen medical catheters

WooSang Shin, Jinhyuk Yun, Dong Yun Choi, Iljeok Kim et al.
Journal of Industrial Information Integration
Industrial Vision Systems and Defect Detection
article

Implicit dimension measurement for automated cross-sectional inspection of multi-lumen medical catheters

WooSang Shin, Jinhyuk Yun, Dong Yun Choi, Iljeok Kim, Jong‐Hyeon Lee
article en

Abstract

Ensuring the cross-sectional shape integrity of medical catheters is a necessity for their safe and effective clinical functionality. Although visual inspection technologies have advanced rapidly, automated inspection of catheter tubes remains challenging due to the complex, deformable structures resulting from extrusion processes and the inherent properties of the materials. In this study, we introduce the Implicit Dimension Measurement (IDiM) framework, which combines rule-based expertise with a data-driven Endpoint Alignment Model (EAM). By parameterizing cross-sectional dimensions using carefully defined endpoints and reference points, IDiM robustly infers key geometric features—even under moderate deformations. We validate its measurement accuracy on multi-lumen catheters (2-, 3-, and 4-lumen) through a high-resolution imaging setup deployed on an actual production line. Experimental results demonstrate measurement precision within five pixels of inter-annotator deviation, comparable to that of human inspectors, along with reliable detection of severe deformation cases via an anomaly detection approach. These findings highlight the practical feasibility of IDiM for high-fidelity shape inspection in medical manufacturing and suggest its broader applicability to other industries requiring precise dimensional verification. • Proposes IDiM for automated cross-sectional inspection of medical catheter. • Introduces EAM to mitigate ambiguities from symmetry and deformation. • Validated through production systems and microscale imaging experiments. • Achieves human-level accuracy with precision below 50 μ m .

Journal of Industrial Information IntegrationVol. 48
SK Group (United States) (US), Kyungpook National University (KR), Pohang TechnoPark (South Korea) (KR), Korea University of Science and Technology (KR), Korea Institute of Industrial Technology (KR)
Openalex Percentile: Top 56%
Industrial Vision Systems and Defect Detection
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